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Prof. Mingguo Ma
School of Geographical Sciences, Southwest University, Chongqing China

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0 Carbon Cycle
0 Climate Change
0 Remote Sensing
0 Land surface process simulation
0 Water use efficienc

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Regular article
Published: 29 August 2021 in Plant and Soil
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Vegetation dynamics play a dominant role in the global carbon cycle and climate, especially in vulnerable karst ecosystem. Many studies have examined the past several decades changes in vegetation greenness and the associated with climate drivers. Yet, few studies have analyzed the vegetation change in global karst regions particularly in the last decades when climate change and anthropogenic disturbance widely occurred. In this study, we investigated the spatio-temporal variations in vegetation dynamic using the Seasonally Integrated Normalized Difference Vegetation Index (SINDVI) and examined their relationship with climate changes using correlation analysis, the ordinary least squares method investigate the variation trends and the Mann-Kendal test to detect the turning points from 2001 to 2020. As expected, there were greening trends in global karst SINDVI from 2001 to 2020, with significant increasing trends in China (range = 0.836, P < 0.05), Europe (range = 0.456, P < 0.05) and many other regions. According to correlation analyses, SINDVI was water-limited in arid and semi-arid regions, such as Middle East and central Asia, and temperature-limited in northern high-latitude. Our results suggest that anthropogenic activities were mainly responsible for the increasing vegetation greenness in tailoring management measures (e.g., Ecological Engineering, the Grain to Green Project) in China and Europe, and intensive farm in Middle East. Coupling warming temperature and increasing precipitation, southeastern Asia and Russia showed increasing trends in SINDVI. In general, climate factors were the dominant drivers for the variation in vegetation greenness in globally karst regions during research period.

ACS Style

Jing Huang; Zhongxi Ge; Yuqing Huang; Xuguang Tang; Zhan Shi; Peiyu Lai; Zengjing Song; Binfei Hao; Hong Yang; Mingguo Ma. Climate change and ecological engineering jointly induced vegetation greening in global karst regions from 2001 to 2020. Plant and Soil 2021, 1 -20.

AMA Style

Jing Huang, Zhongxi Ge, Yuqing Huang, Xuguang Tang, Zhan Shi, Peiyu Lai, Zengjing Song, Binfei Hao, Hong Yang, Mingguo Ma. Climate change and ecological engineering jointly induced vegetation greening in global karst regions from 2001 to 2020. Plant and Soil. 2021; ():1-20.

Chicago/Turabian Style

Jing Huang; Zhongxi Ge; Yuqing Huang; Xuguang Tang; Zhan Shi; Peiyu Lai; Zengjing Song; Binfei Hao; Hong Yang; Mingguo Ma. 2021. "Climate change and ecological engineering jointly induced vegetation greening in global karst regions from 2001 to 2020." Plant and Soil , no. : 1-20.

Research article
Published: 24 August 2021 in Journal of the Indian Society of Remote Sensing
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Modelling in the changes of land use studies is essential towards investigating the regional land cover dynamics as well as for preparing sustainable land use planning and management. In considering the physiographic, climatic, and geospatial development factors, this study simulated and predicted the changes of land use over the southeastern region of Bangladesh integrated of non-spatially demand module and geospatially explicit distribution using CLUE-S model. Globally validated FROM-GLC land cover products of the year 2010, 2015, and 2017 were used as datasets. The simulated maps for 2015 and 2017 have been confirmed to be generally accurate with the actual land use using an error matrix and Kappa indices as of 62.38% (0.6101) and 71.64% (0.7106), respectively, to ensure model simulations success. However, the land-use scenarios between 2017 and 2025 were predicted assuming three modes of development as of existing trends, under forests protection, and of croplands protection. All three scenarios primarily predicted that urbanization, as well as built-up areas, would expand by 100%, 65.3%, and 34.2%, respectively in accumulated all other land-use types. This expansion is predicted as the leading land-use conversions in the study area and expected to an extensive loss of forest in hilly areas and water and croplands in the flat areas. The rate of uneven expansion might be controlled under strict forestland or cropland protection or as well inclusive implementations. The conversion trends of grasslands, wetlands, and others land use in the simulation processes were more subtle. Scientific information derived from simulations revealed the model approach is similarly suitable in formulating relevant land-use policies.

ACS Style

Shahidul Islam; Yuechen Li; Mingguo Ma; Anxu Chen; Zhongxi Ge. Simulation and Prediction of the Spatial Dynamics of Land Use Changes Modelling Through CLUE-S in the Southeastern Region of Bangladesh. Journal of the Indian Society of Remote Sensing 2021, 1 -23.

AMA Style

Shahidul Islam, Yuechen Li, Mingguo Ma, Anxu Chen, Zhongxi Ge. Simulation and Prediction of the Spatial Dynamics of Land Use Changes Modelling Through CLUE-S in the Southeastern Region of Bangladesh. Journal of the Indian Society of Remote Sensing. 2021; ():1-23.

Chicago/Turabian Style

Shahidul Islam; Yuechen Li; Mingguo Ma; Anxu Chen; Zhongxi Ge. 2021. "Simulation and Prediction of the Spatial Dynamics of Land Use Changes Modelling Through CLUE-S in the Southeastern Region of Bangladesh." Journal of the Indian Society of Remote Sensing , no. : 1-23.

Communication
Published: 19 July 2021 in Remote Sensing
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Land surface temperature (LST) is a crucial input parameter in the study of land surface water and energy budgets at local and global scales. Because of cloud obstruction, there are many gaps in thermal infrared remote sensing LST products. To fill these gaps, an improved LST reconstruction method for cloud-covered pixels was proposed by building a linking model for the moderate resolution imaging spectroradiometer (MODIS) LST with other surface variables with a random forest regression method. The accumulated solar radiation from sunrise to satellite overpass collected from the surface solar irradiance product of the Feng Yun-4A geostationary satellite was used to represent the impact of cloud cover on LST. With the proposed method, time-series gap-free LST products were generated for Chongqing City as an example. The visual assessment indicated that the reconstructed gap-free LST images can sufficiently capture the LST spatial pattern associated with surface topography and land cover conditions. Additionally, the validation with in situ observations revealed that the reconstructed cloud-covered LSTs have similar performance as the LSTs on clear-sky days, with the correlation coefficients of 0.92 and 0.89, respectively. The unbiased root mean squared error was 2.63 K. In general, the validation work confirmed the good performance of this approach and its good potential for regional application.

ACS Style

Yao Xiao; Wei Zhao; Mingguo Ma; Kunlong He. Gap-Free LST Generation for MODIS/Terra LST Product Using a Random Forest-Based Reconstruction Method. Remote Sensing 2021, 13, 2828 .

AMA Style

Yao Xiao, Wei Zhao, Mingguo Ma, Kunlong He. Gap-Free LST Generation for MODIS/Terra LST Product Using a Random Forest-Based Reconstruction Method. Remote Sensing. 2021; 13 (14):2828.

Chicago/Turabian Style

Yao Xiao; Wei Zhao; Mingguo Ma; Kunlong He. 2021. "Gap-Free LST Generation for MODIS/Terra LST Product Using a Random Forest-Based Reconstruction Method." Remote Sensing 13, no. 14: 2828.

Journal article
Published: 17 June 2021 in Agricultural and Forest Meteorology
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The peak of the growing season (POS) is known to play an important role in regulating the interannual variability of terrestrial carbon sequestration. Recent research has developed several models for POS estimation; however, a comprehensive understanding of the predictive ability of these models remains lacking, especially taking various biomes and satellite data from different sensors into account. Using data from 54 eddy covariance (EC) flux sites (434 site-years in total) from the FLUXNET2015 dataset, we extracted POS from the normalized difference vegetation index (NDVI), derived from two sensors (MODIS and SPOT-VGT), using four different methods. We then compared the model outputs when data from the different sensors were used, and across different biomes. Our results show that the model predictions correlated weakly (R2 < 0.4) with the flux-based POS when multiple biomes were considered together. However, the performance of the models varied significantly between the models, the sensors that provided the data, and different biomes. Firstly, the more recently proposed methods did not perform as expected, and some of them performed even worse than the commonly used approach. Secondly, POS modeled from MODIS data performed slightly better than that from SPOT-VGT data. Thirdly, when the models are combined, they can reliably estimate POS for grasslands, deciduous broadleaf forests, and open shrublands, but not necessarily for other biomes. Lastly, our results indicate that NDVI-based POS is not a good proxy of flux-based POS. The study suggests that both biomes and sensor properties should be taken into account when estimating POS, and a rigorous validation is necessary before different models are implemented at regional, or larger scales. Therefore, this study provides insights that are helpful for improving our understanding of the impacts of algorithms, sensors, and biomes on model estimates of POS.

ACS Style

Zhongxi Ge; Jing Huang; Xufeng Wang; Yinjun Zhao; Xuguang Tang; Yun Zhou; Peiyu Lai; Binfei Hao; Mingguo Ma. Using remote sensing to identify the peak of the growing season at globally-distributed flux sites: A comparison of models, sensors, and biomes. Agricultural and Forest Meteorology 2021, 307, 108489 .

AMA Style

Zhongxi Ge, Jing Huang, Xufeng Wang, Yinjun Zhao, Xuguang Tang, Yun Zhou, Peiyu Lai, Binfei Hao, Mingguo Ma. Using remote sensing to identify the peak of the growing season at globally-distributed flux sites: A comparison of models, sensors, and biomes. Agricultural and Forest Meteorology. 2021; 307 ():108489.

Chicago/Turabian Style

Zhongxi Ge; Jing Huang; Xufeng Wang; Yinjun Zhao; Xuguang Tang; Yun Zhou; Peiyu Lai; Binfei Hao; Mingguo Ma. 2021. "Using remote sensing to identify the peak of the growing season at globally-distributed flux sites: A comparison of models, sensors, and biomes." Agricultural and Forest Meteorology 307, no. : 108489.

Journal article
Published: 16 June 2021 in Remote Sensing
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The accurate and timely estimation of regional crop biomass at different growth stages is of great importance in guiding crop management decision making. The recent availability of long time series of remote sensing data offers opportunities for crop monitoring. In this paper, four machine learning models, namely random forest (RF), support vector machine (SVM), artificial neural network (ANN), and extreme gradient boosting (XGBoost) were adopted to estimate the seasonal corn biomass based on field observation data and moderate resolution imaging spectroradiometer (MODIS) reflectance data from 2012 to 2019 in the middle reaches of the Heihe River basin, China. Nine variables were selected with the forward feature selection approach from among twenty-seven variables potentially influencing corn biomass: soil-adjusted total vegetation index (SATVI), green ratio vegetation index (GRVI), Nadir_B7 (2105–2155 nm), Nadir_B6 (1628–1652 nm), land surface water index (LSWI), normalized difference vegetation index (NDVI), Nadir_B4 (545–565 nm), and Nadir_B3 (459–479 nm). The results indicated that the corn biomass was suitably estimated (the coefficient of determination (R2) was between 0.72 and 0.78) with the four machine learning models. The XGBoost model performed better than the other three models (R2 = 0.78, root mean squared error (RMSE) = 2.86 t/ha and mean absolute error (MAE) = 1.86 t/ha). Moreover, the RF model was an effective method (R2 = 0.77, RMSE = 2.91 t/ha and MAE = 1.91 t/ha), with a performance comparable to that of the XGBoost model. This study provides a reference for estimating crop biomass from MOD43A4 datasets. In addition, the research demonstrates the potential of machine learning techniques to achieve a relatively accurate estimation of daily corn biomass at a large scale.

ACS Style

Liying Geng; Tao Che; Mingguo Ma; Junlei Tan; Haibo Wang. Corn Biomass Estimation by Integrating Remote Sensing and Long-Term Observation Data Based on Machine Learning Techniques. Remote Sensing 2021, 13, 2352 .

AMA Style

Liying Geng, Tao Che, Mingguo Ma, Junlei Tan, Haibo Wang. Corn Biomass Estimation by Integrating Remote Sensing and Long-Term Observation Data Based on Machine Learning Techniques. Remote Sensing. 2021; 13 (12):2352.

Chicago/Turabian Style

Liying Geng; Tao Che; Mingguo Ma; Junlei Tan; Haibo Wang. 2021. "Corn Biomass Estimation by Integrating Remote Sensing and Long-Term Observation Data Based on Machine Learning Techniques." Remote Sensing 13, no. 12: 2352.

Original article
Published: 07 June 2021 in Journal of Geographical Systems
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A topological relation may be described by different words, and the significance of topological relation models lies in clarifying the semantics of topological relations among different users. Metric details beyond topological relations can help enhance topological relations with qualitative metric information. Although considerable research has been conducted to study topological relation models and metric details, further work is required to consider detailed metrics for topological relation models between lines. Both simple line and nonsimple line are defined, and topological relations for both simple and nonsimple lines have been studied in this research. Metric details for line-line topological relations are elaborated. A topological relation model between lines considering metric details (TRM-MD), including the length ratio and the number-of-endpoints ratio, is proposed in this article. A total of 76 topological relations represented by the TRM-MD and corresponding geometric interpretation is shown, and the inverse relations between these topological relations are also presented. The relationships between the TRM-MD and other topological relation models are discussed. A case study is designed, and the topological relations between bus routes represented by the 9IM, DE-9IM, and TRM-MD are shown to verify the validity of the TRM-MD. The results show the following: (1) both topological relations and metric details can be distinguished by the TRM-MD, and (2) the TRM-MD can be converted to the 9IM and DE-9IM without additional calculations.

ACS Style

Jingwei Shen; Dongzhe Zhao; Kaifang Shi; Mingguo Ma. A model for representing topological relations between lines considering metric details. Journal of Geographical Systems 2021, 23, 407 -424.

AMA Style

Jingwei Shen, Dongzhe Zhao, Kaifang Shi, Mingguo Ma. A model for representing topological relations between lines considering metric details. Journal of Geographical Systems. 2021; 23 (3):407-424.

Chicago/Turabian Style

Jingwei Shen; Dongzhe Zhao; Kaifang Shi; Mingguo Ma. 2021. "A model for representing topological relations between lines considering metric details." Journal of Geographical Systems 23, no. 3: 407-424.

Journal article
Published: 22 March 2021 in Journal of Geophysical Research: Atmospheres
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In the Aral Sea region, significant land use/cover change (LUCC) occurred in the past 50 years, especially the shrinking of Aral Sea due to unreasonable usage of water resources under intensified agricultural activities. However, to date, regional climatic feedbacks on fine‐scale exerted by such LUCC in Central Asia have not been studied clearly. In this study, ALARO‐SURFEX regional climate model was used to perform climate simulations under different underlying surface scenarios with 4 km spatial resolution to explore the impacts of historical LUCC on summer climate during 1980–2015. Our results show that compared to default land surface conditions, the modified ones improved the model's ability in simulating temperature, precipitation, and surface energy fluxes. During the period 1980–2015, LUCC accelerated the warming trend, reduced the summer precipitation and altered allocation of surface energy fluxes. Exposed dry bottom of Aral Sea has undergone the most conspicuous warming, which caused increase of the 2 m maximum temperature, average temperature, and diurnal temperature range by 2.56 ± 0.88°C, 1.04 ± 0.53°C, and 3.42 ± 1.10°C, respectively, while minimum temperature decrease by 1.14 ± 0.56°C. The summer precipitation (mainly convective precipitation) decreased by about 2.33 mm overlay the exposed dry bottom of Aral Sea and approximately 400 km “buffer” region in its eastern side. Additionally, the energy balance changed as follows: −47.9, 50.19, −78.67, and −23.72 W m−2 for net radiation, sensible heat, latent heat, and soil heat, respectively. Quantified contribution of LUCC on regional climate provides useful information for developing mitigation and adaption strategies under the global warming threat.

ACS Style

Huili He; Rafiq Hamdi; Peng Cai; Geping Luo; Friday Uchenna Ochege; Miao Zhang; Piet Termonia; Philippe De Maeyer; Chaofan Li. Impacts of Historical Land Use/Cover Change (1980–2015) on Summer Climate in the Aral Sea Region. Journal of Geophysical Research: Atmospheres 2021, 126, 1 .

AMA Style

Huili He, Rafiq Hamdi, Peng Cai, Geping Luo, Friday Uchenna Ochege, Miao Zhang, Piet Termonia, Philippe De Maeyer, Chaofan Li. Impacts of Historical Land Use/Cover Change (1980–2015) on Summer Climate in the Aral Sea Region. Journal of Geophysical Research: Atmospheres. 2021; 126 (6):1.

Chicago/Turabian Style

Huili He; Rafiq Hamdi; Peng Cai; Geping Luo; Friday Uchenna Ochege; Miao Zhang; Piet Termonia; Philippe De Maeyer; Chaofan Li. 2021. "Impacts of Historical Land Use/Cover Change (1980–2015) on Summer Climate in the Aral Sea Region." Journal of Geophysical Research: Atmospheres 126, no. 6: 1.

Journal article
Published: 21 January 2021 in Remote Sensing
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Capturing the spatial heterogeneity and characteristic scale is the key to determining the spatial patterns of land surfaces. In this research, the core observation area of the middle reaches of the Heihe River Basin was selected as the study area, and the scale identification of several typical objects was carried out by implementing experiments on moderate- and high-resolution remotely sensed ASTER and CASI NDVI images. The aim was to evaluate the potential of the local variance and semivariance analysis to characterize the spatial heterogeneity of objects, track their changes with scale, and obtain their scales. Our results show that natural objects have multiscale structures. For a single object with a recognizable size, the results of the two methods are relatively consistent. For continuously distributed samples of indistinctive size, the scale obtained by the local variance is smaller than that obtained by the semivariance. As the image resolution becomes coarser and the research scopes expand, the scales of objects are also increasing. This article also indicates that with a small research area of uniform objects, the local variance and semivariance are easy to facilitate researchers to quickly select the appropriate spatial resolution of remote sensing data according to the research area.

ACS Style

Xiuyi Wu; Wenping Yu; Jinan Shi; Mingguo Ma; Xiaolu Li; Wenjian Wu. Identification of the Characteristic Scale of Fine Ground Objects: A Case Study of the Core Observation Area in the Middle Reaches of the Heihe River Basin. Remote Sensing 2021, 13, 362 .

AMA Style

Xiuyi Wu, Wenping Yu, Jinan Shi, Mingguo Ma, Xiaolu Li, Wenjian Wu. Identification of the Characteristic Scale of Fine Ground Objects: A Case Study of the Core Observation Area in the Middle Reaches of the Heihe River Basin. Remote Sensing. 2021; 13 (3):362.

Chicago/Turabian Style

Xiuyi Wu; Wenping Yu; Jinan Shi; Mingguo Ma; Xiaolu Li; Wenjian Wu. 2021. "Identification of the Characteristic Scale of Fine Ground Objects: A Case Study of the Core Observation Area in the Middle Reaches of the Heihe River Basin." Remote Sensing 13, no. 3: 362.

Journal article
Published: 14 January 2021 in Agricultural and Forest Meteorology
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The global gross primary productivity (GPP) product derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) is perhaps the most widely used GPP product. However, there is still a large uncertainty associated with the MODIS GPP product partly due to the uncertainty in the default Biome specified Parameters Look-Up Table (BPLUT) of the MODIS photosynthesis (PSN) model. Here, we used the Bayesian inference with the Markov chain Monte Carlo (MCMC) approach and FLUXNET data from 110 sites to estimate the parameters of the MODIS PSN model (maximum light use efficiency: ɛmax; temperature scalar-related parameters: Tminmin and Tminmax; water scalar-related parameters: VPDmin and VPDmax) through individual and joint optimization. The spread of the posterior probability density function (PDF) of the parameters allowed for the calculation of parameter means and uncertainty estimates and also provided information on the behavior of the parameters. Each model parameter varied not only across sites but also across plant functional types (PFTs). The means of the optimized parameter values within each PFT were used to update the BPLUT. We also generated parameter estimates for wetlands and C4/C3 croplands in the BPLUT. Parameters from the joint optimization were more representative and less variable. The optimization improved the performance of the MODIS PSN model by 15% for deciduous broadleaf forests, 8% for savannas, and 3% for grasslands with well-constrained parameters. The performance of the optimized model depended on the effectiveness of parameter optimization. Our study is an effort towards quantifying and reducing parameter uncertainty of the MODIS PSN model and improving the global MODIS GPP product for better understanding global ecosystem carbon dynamics, plant productivity, and carbon-climate feedbacks.

ACS Style

Xiaojuan Huang; Jingfeng Xiao; Xufeng Wang; Mingguo Ma. Improving the global MODIS GPP model by optimizing parameters with FLUXNET data. Agricultural and Forest Meteorology 2021, 300, 108314 .

AMA Style

Xiaojuan Huang, Jingfeng Xiao, Xufeng Wang, Mingguo Ma. Improving the global MODIS GPP model by optimizing parameters with FLUXNET data. Agricultural and Forest Meteorology. 2021; 300 ():108314.

Chicago/Turabian Style

Xiaojuan Huang; Jingfeng Xiao; Xufeng Wang; Mingguo Ma. 2021. "Improving the global MODIS GPP model by optimizing parameters with FLUXNET data." Agricultural and Forest Meteorology 300, no. : 108314.

Review
Published: 07 January 2021 in Sustainability
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Paddy rice is a staple food of three billion people in the world. Timely and accurate estimation of the paddy rice planting area and paddy rice yield can provide valuable information for the government, planners and decision makers to formulate policies. This article reviews the existing paddy rice mapping methods presented in the literature since 2010, classifies these methods, and analyzes and summarizes the basic principles, advantages and disadvantages of these methods. According to the data sources used, the methods are divided into three categories: (I) Optical mapping methods based on remote sensing; (II) Mapping methods based on microwave remote sensing; and (III) Mapping methods based on the integration of optical and microwave remote sensing. We found that the optical remote sensing data sources are mainly MODIS, Landsat, and Sentinel-2, and the emergence of Sentinel-1 data has promoted research on radar mapping methods for paddy rice. Multisource data integration further enhances the accuracy of paddy rice mapping. The best methods are phenology algorithms, paddy rice mapping combined with machine learning, and multisource data integration. Innovative methods include the time series similarity method, threshold method combined with mathematical models, and object-oriented image classification. With the development of computer technology and the establishment of cloud computing platforms, opportunities are provided for obtaining large-scale high-resolution rice maps. Multisource data integration, paddy rice mapping under different planting systems and the connection with global changes are the focus of future development priorities.

ACS Style

Rongkun Zhao; Yuechen Li; Mingguo Ma. Mapping Paddy Rice with Satellite Remote Sensing: A Review. Sustainability 2021, 13, 503 .

AMA Style

Rongkun Zhao, Yuechen Li, Mingguo Ma. Mapping Paddy Rice with Satellite Remote Sensing: A Review. Sustainability. 2021; 13 (2):503.

Chicago/Turabian Style

Rongkun Zhao; Yuechen Li; Mingguo Ma. 2021. "Mapping Paddy Rice with Satellite Remote Sensing: A Review." Sustainability 13, no. 2: 503.

Journal article
Published: 16 December 2020 in Agricultural and Forest Meteorology
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Water use efficiency (WUE), the ratio of carbon gain to water loss, is an important physiological indicator in assessing the interactions between the carbon and water cycles. Quantifying the spatiotemporal patterns of WUE at both canopy and ecosystem levels across main dryland ecosystems along climate gradients is vital for water resource management in water-limited regions. However, the patterns of WUE among natural and managed ecosystems in drylands are not well understood. We partitioned the evapotranspiration (ET) components and calculated WUE at both canopy and ecosystem levels across dryland ecosystems in an arid region in Northwest China using observations from a regional flux observation network. Our results showed divergent variations of canopy and ecosystem WUE across the main ecosystems along climate gradients in arid regions. The ecosystem WUE and canopy WUE ranged from 0.7 to 1.8 gC Kg−1 H2O and from 0.8 to 2.4 gC Kg−1 H2O, respectively. Climatic gradients were the dominant factors controlling the spatial patterns of WUE across dryland ecosystems. Divergences were also observed between oasis and natural ecosystems because of different environmental conditions and management practices. Climatic regulation of the spatial WUE patterns was dominated by water variability rather than temperature. The higher canopy WUE for desert ecosystems indicated that these ecosystems were adapted to the water-limited environment. The discrepancies of both canopy and ecosystem WUE between croplands in arid and hyper-arid climate zones were caused by the differences in agricultural management techniques for these crops. Human activities (e.g., irrigation and agriculture management) altered the distribution of water resource and water use strategies, which further affected the magnitude and patterns of WUE in drylands. This study provides insights into the spatiotemporal patterns of ET components and both canopy and ecosystem WUE over dryland ecosystems and can inform regional water resources management in water-limited regions.

ACS Style

Haibo Wang; Xin Li; Jingfeng Xiao; Mingguo Ma. Evapotranspiration components and water use efficiency from desert to alpine ecosystems in drylands. Agricultural and Forest Meteorology 2020, 298-299, 108283 .

AMA Style

Haibo Wang, Xin Li, Jingfeng Xiao, Mingguo Ma. Evapotranspiration components and water use efficiency from desert to alpine ecosystems in drylands. Agricultural and Forest Meteorology. 2020; 298-299 ():108283.

Chicago/Turabian Style

Haibo Wang; Xin Li; Jingfeng Xiao; Mingguo Ma. 2020. "Evapotranspiration components and water use efficiency from desert to alpine ecosystems in drylands." Agricultural and Forest Meteorology 298-299, no. : 108283.

Journal article
Published: 07 October 2020 in Remote Sensing
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Nighttime light data play an important role in the research on cities, while the urban centers over a large spatial scale are still far from clearly understood. Aiming at the current challenges in monitoring the spatial structure of cities using nighttime light data, this paper proposes a new method for identifying urban centers for massive cities at the large spatial scale based on the brightness information captured by the Suomi National Polar-Orbiting Partnership’s Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) sensor. Based on the method for extracting the peak point based on digital elevation model (DEM) data in terrain analysis, the maximum neighborhood and difference algorithms were applied to the NPP-VIIRS data to extract the pixels with the peak nighttime light intensity to identify the potential locations of urban centers. The results show 7239 urban centers in 2200 cities in China in 2017, with an average of 3.3 urban centers per city. Approximately 68% of the cities had significant polycentric structures. The developed method in this paper is useful for identifying the urban centers and can provide the reference to the city planning and construction.

ACS Style

Mingguo Ma; Qin Lang; Hong Yang; Kaifang Shi; Wei Ge. Identification of Polycentric Cities in China Based on NPP-VIIRS Nighttime Light Data. Remote Sensing 2020, 12, 3248 .

AMA Style

Mingguo Ma, Qin Lang, Hong Yang, Kaifang Shi, Wei Ge. Identification of Polycentric Cities in China Based on NPP-VIIRS Nighttime Light Data. Remote Sensing. 2020; 12 (19):3248.

Chicago/Turabian Style

Mingguo Ma; Qin Lang; Hong Yang; Kaifang Shi; Wei Ge. 2020. "Identification of Polycentric Cities in China Based on NPP-VIIRS Nighttime Light Data." Remote Sensing 12, no. 19: 3248.

Journal article
Published: 22 August 2020 in Land
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As an important component to quantify the carbon budget, accurate evaluation of terrestrial gross primary production (GPP) is crucial for large-scale applications, especially in dryland ecosystems. Based on the in situ data from six flux sites in northwestern China from 2014 to 2016, this study compares seasonal and interannual dynamics of carbon fluxes between these arid and semi-arid ecosystems and the atmosphere. Meanwhile, the reliability of multiple remotely-derived GPP products in representative drylands was examined, including the Breathing Earth System Simulator (BESS), the Moderate Resolution Imaging Spectroradiometer (MODIS) and data derived from the OCO-2 solar-induced chlorophyll fluorescence (GOSIF). The results indicated that the carbon fluxes had clear seasonal patterns, with all ecosystems functioning as carbon sinks. The maize cropland had the highest GPP with 1183 g C m−2 y−1. Although the net ecosystem carbon exchange (NEE) in the Tamarix spp. ecosystem was the smallest among these flux sites, it reached 208 g C m−2 y−1. Furthermore, distinct advantages of GOSIF GPP (with R2 = 0.85–0.98, and RMSE = 0.87–2.66 g C m−2 d−1) were found with good performance. However, large underestimations in three GPP products existed during the growing seasons, except in grassland ecosystems. The main reasons can be ascribed to the uncertainties in the key model parameters, including the underestimated light use efficiency of the MODIS GPP, the same coarse land cover product for the BESS and MODIS GPP, the coarse gridded meteorological data, and distribution of C3 and C4 plants. Therefore, it still requires more work to accurately quantify the GPP across these dryland ecosystems.

ACS Style

Qing Gu; Hui Zheng; Li Yao; Min Wang; Mingguo Ma; Xufeng Wang; Xuguang Tang. Performance of the Remotely-Derived Products in Monitoring Gross Primary Production across Arid and Semi-Arid Ecosystems in Northwest China. Land 2020, 9, 288 .

AMA Style

Qing Gu, Hui Zheng, Li Yao, Min Wang, Mingguo Ma, Xufeng Wang, Xuguang Tang. Performance of the Remotely-Derived Products in Monitoring Gross Primary Production across Arid and Semi-Arid Ecosystems in Northwest China. Land. 2020; 9 (9):288.

Chicago/Turabian Style

Qing Gu; Hui Zheng; Li Yao; Min Wang; Mingguo Ma; Xufeng Wang; Xuguang Tang. 2020. "Performance of the Remotely-Derived Products in Monitoring Gross Primary Production across Arid and Semi-Arid Ecosystems in Northwest China." Land 9, no. 9: 288.

Review
Published: 05 August 2020 in Water
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Separating the impact of climate change and human activities on runoff is an important topic in hydrology, and a large number of methods and theories have been widely used. In this paper, we review the current papers on separating the impacts of climate and human activities on runoff, summarize the progress of relevant research methods and applications in recent years, and discuss future research needs and directions.

ACS Style

Feng Zeng; Ming-Guo Ma; Dong-Rui Di; Wei-Yu Shi. Separating the Impacts of Climate Change and Human Activities on Runoff: A Review of Method and Application. Water 2020, 12, 2201 .

AMA Style

Feng Zeng, Ming-Guo Ma, Dong-Rui Di, Wei-Yu Shi. Separating the Impacts of Climate Change and Human Activities on Runoff: A Review of Method and Application. Water. 2020; 12 (8):2201.

Chicago/Turabian Style

Feng Zeng; Ming-Guo Ma; Dong-Rui Di; Wei-Yu Shi. 2020. "Separating the Impacts of Climate Change and Human Activities on Runoff: A Review of Method and Application." Water 12, no. 8: 2201.

Journal article
Published: 12 June 2020 in IEEE Geoscience and Remote Sensing Letters
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Accurate information about the airport's throughput is crucial for monitoring traffic flow, evaluating the development status of aviation industry. In this letter, we used the Suomi National Polar-orbiting Partnership-Visible Infrared Imaging Radiometer (NPP-VIIRS) and the Defense Meteorological Satellite Program's Operational Linescan System (DMSP-OLS) nighttime light (NTL) data as effective proxies for evaluating and monitoring the airport's throughput power in China mainland. The results show that there is a significant positive correlation between the NTL intensity and the airport's throughput (R² > 0.85). The NPP-VIIRS NTL data have been proved to not only distinguish the airport area and nonairport area but also build airport's NTL feature space. This letter reveals that the NTL images provide powerful remote sensing data sources to model the spatiotemporal dynamics of airport's throughput of China mainland at a large spatial scale.

ACS Style

Mingguo Ma; Wei Ge; Kaifang Shi. Airport’s Throughput Estimation Using Nighttime Light Data in China Mainland. IEEE Geoscience and Remote Sensing Letters 2020, 18, 1357 -1360.

AMA Style

Mingguo Ma, Wei Ge, Kaifang Shi. Airport’s Throughput Estimation Using Nighttime Light Data in China Mainland. IEEE Geoscience and Remote Sensing Letters. 2020; 18 (8):1357-1360.

Chicago/Turabian Style

Mingguo Ma; Wei Ge; Kaifang Shi. 2020. "Airport’s Throughput Estimation Using Nighttime Light Data in China Mainland." IEEE Geoscience and Remote Sensing Letters 18, no. 8: 1357-1360.

Journal article
Published: 03 June 2020 in ISPRS International Journal of Geo-Information
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Gridded population results at a fine resolution are important for optimizing the allocation of resources and researching population migration. For example, the data are crucial for epidemic control and natural disaster relief. In this study, the random forest model was applied to multisource data to estimate the population distribution in impervious areas at a 30 m spatial resolution in Chongqing, Southwest China. The community population data from the Chinese government were used to validate the estimation accuracy. Compared with the other regression techniques, the random forest regression method produced more accurate results (R2 = 0.7469, RMSE = 2785.04 and p < 0.01). The points of interest (POIs) data played a more important role in the population estimation than the nighttime light images and natural topographical data, particularly in urban settings. Our results support the wide application of our method in mapping densely populated cities in China and other countries with similar characteristics.

ACS Style

Yun Zhou; Mingguo Ma; Kaifang Shi; Zhenyu Peng. Estimating and Interpreting Fine-Scale Gridded Population Using Random Forest Regression and Multisource Data. ISPRS International Journal of Geo-Information 2020, 9, 369 .

AMA Style

Yun Zhou, Mingguo Ma, Kaifang Shi, Zhenyu Peng. Estimating and Interpreting Fine-Scale Gridded Population Using Random Forest Regression and Multisource Data. ISPRS International Journal of Geo-Information. 2020; 9 (6):369.

Chicago/Turabian Style

Yun Zhou; Mingguo Ma; Kaifang Shi; Zhenyu Peng. 2020. "Estimating and Interpreting Fine-Scale Gridded Population Using Random Forest Regression and Multisource Data." ISPRS International Journal of Geo-Information 9, no. 6: 369.

Journal article
Published: 09 March 2020 in Sustainability
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While a number of machine learning (ML) models have been used to estimate RE, systematic evaluation and comparison of these models are still limited. In this study, we developed three traditional ML models and a deep learning (DL) model, stacked autoencoders (SAE), to estimate RE in northern China’s grasslands. The four models were trained with two strategies: training for all of northern China’s grasslands and separate training for the alpine and temperate grasslands. Our results showed that all four ML models estimated RE in northern China’s grasslands fairly well, while the SAE model performed best (R2 = 0.858, RMSE = 0.472 gC m−2 d−1, MAE = 0.304 gC m−2 d−1). Models trained with the two strategies had almost identical performances. The enhanced vegetation index and soil organic carbon density (SOCD) were the two most important environmental variables for estimating RE in the grasslands of northern China. Air temperature (Ta) was more important than the growing season land surface water index (LSWI) in the alpine grasslands, while the LSWI was more important than Ta in the temperate grasslands. These findings may promote the application of DL models and the inclusion of SOCD for RE estimates with increased accuracy.

ACS Style

Xiaobo Zhu; Honglin He; Mingguo Ma; Xiaoli Ren; Li Zhang; Fawei Zhang; Yingnian Li; Peili Shi; Shiping Chen; Yanfen Wang; Xiaoping Xin; Yaoming Ma; Yu Zhang; Mingyuan Du; Rong Ge; Na Zeng; Pan Li; Zhongen Niu; Liyun Zhang; Yan Lv; Zengjing Song; Qing Gu. Estimating Ecosystem Respiration in the Grasslands of Northern China Using Machine Learning: Model Evaluation and Comparison. Sustainability 2020, 12, 2099 .

AMA Style

Xiaobo Zhu, Honglin He, Mingguo Ma, Xiaoli Ren, Li Zhang, Fawei Zhang, Yingnian Li, Peili Shi, Shiping Chen, Yanfen Wang, Xiaoping Xin, Yaoming Ma, Yu Zhang, Mingyuan Du, Rong Ge, Na Zeng, Pan Li, Zhongen Niu, Liyun Zhang, Yan Lv, Zengjing Song, Qing Gu. Estimating Ecosystem Respiration in the Grasslands of Northern China Using Machine Learning: Model Evaluation and Comparison. Sustainability. 2020; 12 (5):2099.

Chicago/Turabian Style

Xiaobo Zhu; Honglin He; Mingguo Ma; Xiaoli Ren; Li Zhang; Fawei Zhang; Yingnian Li; Peili Shi; Shiping Chen; Yanfen Wang; Xiaoping Xin; Yaoming Ma; Yu Zhang; Mingyuan Du; Rong Ge; Na Zeng; Pan Li; Zhongen Niu; Liyun Zhang; Yan Lv; Zengjing Song; Qing Gu. 2020. "Estimating Ecosystem Respiration in the Grasslands of Northern China Using Machine Learning: Model Evaluation and Comparison." Sustainability 12, no. 5: 2099.

Journal article
Published: 03 March 2020 in Remote Sensing
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Significant impact of extreme droughts on human society and ecosystem has occurred in many places of the world, for example, Southwest China (SWC). Considerable research concentrated on analyzing causes and effects of droughts in SWC, but few studies have examined seasonal indicators, such as variations of surface water and vegetation phenology. With the ongoing satellite missions, more and more earth observation data become available to environmental studies. Exploring the responses of seasonal indicators from satellite data to drought is helpful for the future drought forecast and management. This study analyzed the seasonal responses of surface water and vegetation phenology to drought in SWC using the multi-source data including Seasonal Water Area (SWA), Permanent Water Area (PWA), Start of Season (SOS), End of Season (EOS), Length of Season (LOS), precipitation, temperature, solar radiation, evapotranspiration, the Palmer Drought Severity Index (PDSI), the Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), Gross Primary Productivity (GPP) and data from water conservancy construction. The results showed that SWA and LOS effectively revealed the development and recovery of droughts. There were two obvious drought periods from 2000 to 2017. In the first period (from August 2003 to June 2007), SWA decreased by 11.81% and LOS shortened by 5 days. They reduced by 21.04% and 9 days respectively in the second period (from September 2009 to June 2014), which indicated that there are more severe droughts in the second period. The SOS during two drought periods delayed by 3~6 days in spring, while the EOS advanced 1~3 days in autumn. All of PDSI, SWA and LOS could reflect the period of droughts in SWC, but the LOS and PDSI were very sensitive to the meteorological events, such as precipitation and temperature, while the SWA performed a more stable reaction to drought and could be a good indicator for the drought periodicity. This made it possible for using SWA in drought forecast because of the strong correlation between SWA and drought. Our results improved the understanding of seasonal responses to extreme droughts in SWC, which will be helpful to the drought monitoring and mitigation for different seasons in this ecologically fragile region.

ACS Style

Peiyu Lai; Miao Zhang; Zhongxi Ge; Binfei Hao; Zengjing Song; Jing Huang; Mingguo Ma; Hong Yang; Xujun Han. Responses of Seasonal Indicators to Extreme Droughts in Southwest China. Remote Sensing 2020, 12, 818 .

AMA Style

Peiyu Lai, Miao Zhang, Zhongxi Ge, Binfei Hao, Zengjing Song, Jing Huang, Mingguo Ma, Hong Yang, Xujun Han. Responses of Seasonal Indicators to Extreme Droughts in Southwest China. Remote Sensing. 2020; 12 (5):818.

Chicago/Turabian Style

Peiyu Lai; Miao Zhang; Zhongxi Ge; Binfei Hao; Zengjing Song; Jing Huang; Mingguo Ma; Hong Yang; Xujun Han. 2020. "Responses of Seasonal Indicators to Extreme Droughts in Southwest China." Remote Sensing 12, no. 5: 818.

Research article
Published: 01 February 2020 in Journal of Sensors
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The land surface model SURFEX 7.3 was used to study climate effect of urban expansion located in oasis in arid area of Northwest China by surface and 2 m urban heat island (UHI) intensity and available energy ratio (B). We performed a true regional development scenario and three assumed scenario simulations in 1978, 1993, 2004, and 2014, respectively. The results show that 2 m UHI always displays positive twin peaks during whole day, while surface UHI only displays a positive single peak with several hours during daytime at four seasons in the four years. Moreover, 2 m UHI intensity during night is higher than that during daytime, indicating that UHI intensity is contributed more by “trap effect” from urban complex geometry or anthropogenic heat and that surface UHI according to land surface temperature cannot reflect UHI comprehensively. The oasis-urban development resulted in local warming and increasing of B, and compared with the original undeveloped environment, local climate in the study area was in a relatively balanced state in 1978 and 1993 due to the “heating effect” of urban area and the “cooling effect” of oasis, but the offsetting effect from oasis would become weaker after1993.

ACS Style

Miao Zhang; Geping Luo; Peng Cai; Rafiq Hamdi. Effects on Local Temperature and Energy of Oasis City Expansion in Arid Area of Northwest China. Journal of Sensors 2020, 2020, 1 -12.

AMA Style

Miao Zhang, Geping Luo, Peng Cai, Rafiq Hamdi. Effects on Local Temperature and Energy of Oasis City Expansion in Arid Area of Northwest China. Journal of Sensors. 2020; 2020 ():1-12.

Chicago/Turabian Style

Miao Zhang; Geping Luo; Peng Cai; Rafiq Hamdi. 2020. "Effects on Local Temperature and Energy of Oasis City Expansion in Arid Area of Northwest China." Journal of Sensors 2020, no. : 1-12.

Journal article
Published: 10 January 2020 in Atmosphere
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The rapid oasis expansion and urbanization that occurred in Xinjiang province (China) in the last decades have greatly modified the land surface energy balance and influenced the local circulation under the arid mountains-plain background system. In this study, we first evaluated the ALARO regional climate model coupled to the land surface scheme SURFEX at 4 km resolution using 53 national climatological stations and 5 automatic weather stations. We found that the model correctly simulates daily and hourly variation of 2 m temperature and relative humidity. A 4-day clear sky period has been chosen to study both local atmospheric circulations and their mutual interaction. Observations and simulations both show that a low-level divergence over oasis appears between 19:00 and 21:00 Beijing Time when the background mountain-plain wind system is weak. The model simulates a synergistic interaction between the oasis-desert breeze and urban-rural breeze from 16:00 until 22:00 with a maximum effect at 20:00 when the downdraft over oasis (updraft over urban) areas increases by 0.8 (0.4) Pa/s. The results show that the oasis expansion decreases the nocturnal urban heat island in the city of Urumqi by 0.8 °C, while the impact of urban expansion on the oasis cold island is negligible.

ACS Style

Peng Cai; Rafiq Hamdi; Huili He; Geping Luo; Jin Wang; Miao Zhang; Chaofan Li; Piet Termonia; Philippe De Maeyer. Numerical Study of the Interaction between Oasis and Urban Areas within an Arid Mountains-Desert System in Xinjiang, China. Atmosphere 2020, 11, 85 .

AMA Style

Peng Cai, Rafiq Hamdi, Huili He, Geping Luo, Jin Wang, Miao Zhang, Chaofan Li, Piet Termonia, Philippe De Maeyer. Numerical Study of the Interaction between Oasis and Urban Areas within an Arid Mountains-Desert System in Xinjiang, China. Atmosphere. 2020; 11 (1):85.

Chicago/Turabian Style

Peng Cai; Rafiq Hamdi; Huili He; Geping Luo; Jin Wang; Miao Zhang; Chaofan Li; Piet Termonia; Philippe De Maeyer. 2020. "Numerical Study of the Interaction between Oasis and Urban Areas within an Arid Mountains-Desert System in Xinjiang, China." Atmosphere 11, no. 1: 85.