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Guojie Wang
School of Geographical Sciences, Nanjing University of Information Science & Technology Nanjing China

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Journal article
Published: 28 June 2021 in Atmosphere
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The machine learning algorithms application in atmospheric sciences along the Earth System Models has the potential of improving prediction, forecast, and reconstruction of missing data. In the current study, a combination of two machine learning techniques namely K-means, and decision tree (C4.5) algorithms, are used to separate observed precipitation into clusters and classified the associated large-scale circulation indices. Observed precipitation from the Chinese Meteorological Agency (CMA) during 1961–2016 for 83 stations in the Poyang Lake basin (PLB) is used. The results from K-Means clusters show two precipitation clusters splitting the PLB precipitation into a northern and southern cluster, with a silhouette coefficient ~0.5. The PLB precipitation leading cluster (C1) contains 48 stations accounting for 58% of the regional station density, while Cluster 2 (C2) covers 35, accounting for 42% of the stations. The interannual variability in precipitation exhibited significant differences for both clusters. The decision tree (C4.5) is employed to explore the large-scale atmospheric indices from National Climate Center (NCC) associated with each cluster during the preceding spring season as a predictor. The C1 precipitation was linked with the location and intensity of subtropical ridgeline position over Northern Africa, whereas the C2 precipitation was suggested to be associated with the Atlantic-European Polar Vortex Area Index. The precipitation anomalies further validated the results of both algorithms. The findings are in accordance with previous studies conducted globally and hence recommend the applications of machine learning techniques in atmospheric science on a sub-regional and sub-seasonal scale. Future studies should explore the dynamics of the K-Means, and C4.5 derived indicators for a better assessment on a regional scale. This research based on machine learning methods may bring a new solution to climate forecast.

ACS Style

Dan Lou; Mengxi Yang; Dawei Shi; Guojie Wang; Waheed Ullah; Yuanfang Chai; Yutian Chen. K-Means and C4.5 Decision Tree Based Prediction of Long-Term Precipitation Variability in the Poyang Lake Basin, China. Atmosphere 2021, 12, 834 .

AMA Style

Dan Lou, Mengxi Yang, Dawei Shi, Guojie Wang, Waheed Ullah, Yuanfang Chai, Yutian Chen. K-Means and C4.5 Decision Tree Based Prediction of Long-Term Precipitation Variability in the Poyang Lake Basin, China. Atmosphere. 2021; 12 (7):834.

Chicago/Turabian Style

Dan Lou; Mengxi Yang; Dawei Shi; Guojie Wang; Waheed Ullah; Yuanfang Chai; Yutian Chen. 2021. "K-Means and C4.5 Decision Tree Based Prediction of Long-Term Precipitation Variability in the Poyang Lake Basin, China." Atmosphere 12, no. 7: 834.

Journal article
Published: 23 June 2021 in International Journal of Environmental Research and Public Health
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The main goal of this study was to assess the interannual variations and spatial patterns of projected changes in simulated evapotranspiration (ET) in the 21st century over continental Africa based on the latest Shared Socioeconomic Pathways and the Representative Concentration Pathways (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) provided by the France Centre National de Recherches Météorologiques (CNRM-CM) model in the Sixth Phase of Coupled Model Intercomparison Project (CMIP6) framework. The projected spatial and temporal changes were computed for three time slices: 2020–2039 (near future), 2040–2069 (mid-century), and 2080–2099 (end-of-the-century), relative to the baseline period (1995–2014). The results show that the spatial pattern of the projected ET was not uniform and varied across the climate region and under the SSP-RCPs scenarios. Although the trends varied, they were statistically significant for all SSP-RCPs. The SSP5-8.5 and SSP3-7.0 projected higher ET seasonality than SSP1-2.6 and SSP2-4.5. In general, we suggest the need for modelers and forecasters to pay more attention to changes in the simulated ET and their impact on extreme events. The findings provide useful information for water resources managers to develop specific measures to mitigate extreme events in the regions most affected by possible changes in the region’s climate. However, readers are advised to treat the results with caution as they are based on a single GCM model. Further research on multi-model ensembles (as more models’ outputs become available) and possible key drivers may provide additional information on CMIP6 ET projections in the region.

ACS Style

Isaac Nooni; Daniel Hagan; Guojie Wang; Waheed Ullah; Jiao Lu; Shijie Li; Mawuli Dzakpasu; Nana Prempeh; Kenny Lim Kam Sian. Future Changes in Simulated Evapotranspiration across Continental Africa Based on CMIP6 CNRM-CM6. International Journal of Environmental Research and Public Health 2021, 18, 6760 .

AMA Style

Isaac Nooni, Daniel Hagan, Guojie Wang, Waheed Ullah, Jiao Lu, Shijie Li, Mawuli Dzakpasu, Nana Prempeh, Kenny Lim Kam Sian. Future Changes in Simulated Evapotranspiration across Continental Africa Based on CMIP6 CNRM-CM6. International Journal of Environmental Research and Public Health. 2021; 18 (13):6760.

Chicago/Turabian Style

Isaac Nooni; Daniel Hagan; Guojie Wang; Waheed Ullah; Jiao Lu; Shijie Li; Mawuli Dzakpasu; Nana Prempeh; Kenny Lim Kam Sian. 2021. "Future Changes in Simulated Evapotranspiration across Continental Africa Based on CMIP6 CNRM-CM6." International Journal of Environmental Research and Public Health 18, no. 13: 6760.

Journal article
Published: 18 June 2021 in International Journal of Applied Earth Observation and Geoinformation
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Precise monitoring of floods is significant in disaster management and loss reduction; however, remote sensing data resource and methods can largely affect the monitoring accuracy of flooded areas. In this study, we use cloud-free Sentinel-1 Synthetic Aperture Radar (SAR) imagery, preferable to the optical imagery. We have used 5 convolutional neural networks (CNNs), including HRNet, DenseNet, SegNet, ResNet and DeepLab v3 + for flood monitoring in the Poyang Lake area, and compared their performances with the traditional methods — the bimodal threshold segmentation (BTS) and the OSTU method. The HRNet has superior performance in water body identification with the highest precision and efficiency, based on a parallel structure to not only extract rich semantic information but also maintain high-resolution features in the whole process. Besides, speckle noise reduction by deep convolutional neural networks in SAR imagery is better compared with the Refined Lee filter. The CNNs are then used to monitor the temporal evolution of summer flooding (May-Nov.) in 2020. Results show the smallest water coverage of Poyang Lake in late May; it gradually increases to the maximum in mid-July, and then shows a downward trend until November.

ACS Style

Zhen Dong; Guojie Wang; Solomon Obiri Yeboah Amankwah; Xikun Wei; Yifan Hu; Aiqing Feng. Monitoring the summer flooding in the Poyang Lake area of China in 2020 based on Sentinel-1 data and multiple convolutional neural networks. International Journal of Applied Earth Observation and Geoinformation 2021, 102, 102400 .

AMA Style

Zhen Dong, Guojie Wang, Solomon Obiri Yeboah Amankwah, Xikun Wei, Yifan Hu, Aiqing Feng. Monitoring the summer flooding in the Poyang Lake area of China in 2020 based on Sentinel-1 data and multiple convolutional neural networks. International Journal of Applied Earth Observation and Geoinformation. 2021; 102 ():102400.

Chicago/Turabian Style

Zhen Dong; Guojie Wang; Solomon Obiri Yeboah Amankwah; Xikun Wei; Yifan Hu; Aiqing Feng. 2021. "Monitoring the summer flooding in the Poyang Lake area of China in 2020 based on Sentinel-1 data and multiple convolutional neural networks." International Journal of Applied Earth Observation and Geoinformation 102, no. : 102400.

Journal article
Published: 30 April 2021 in Remote Sensing
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Assessing satellite-based precipitation product capacity for detecting precipitation and linear trends is fundamental for accurately knowing precipitation characteristics and changes, especially for regions with scarce and even no observations. In this study, we used daily gauge observations across the Huai River Basin (HRB) during 1983–2012 and four validation metrics to evaluate the Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) capacity for detecting extreme precipitation and linear trends. The PERSIANN-CDR well captured climatologic characteristics of the precipitation amount- (PRCPTOT, R85p, R95p, and R99p), duration- (CDD and CWD), and frequency-based indices (R10mm, R20mm, and Rnnmm), followed by moderate performance for the intensity-based indices (Rx1day, R5xday, and SDII). Based on different validation metrics, the PERSIANN-CDR capacity to detect extreme precipitation varied spatially, and meanwhile the validation metric-based performance differed among these indices. Furthermore, evaluation of the PERSIANN-CDR linear trends indicated that this product had a much limited and even no capacity to represent extreme precipitation changes across the HRB. Briefly, this study provides a significant reference for PERSIANN-CDR developers to use to improve product accuracy from the perspective of extreme precipitation, and for potential users in the HRB.

ACS Style

Shanlei Sun; Jiazhi Wang; Wanrong Shi; Rongfan Chai; Guojie Wang. Capacity of the PERSIANN-CDR Product in Detecting Extreme Precipitation over Huai River Basin, China. Remote Sensing 2021, 13, 1747 .

AMA Style

Shanlei Sun, Jiazhi Wang, Wanrong Shi, Rongfan Chai, Guojie Wang. Capacity of the PERSIANN-CDR Product in Detecting Extreme Precipitation over Huai River Basin, China. Remote Sensing. 2021; 13 (9):1747.

Chicago/Turabian Style

Shanlei Sun; Jiazhi Wang; Wanrong Shi; Rongfan Chai; Guojie Wang. 2021. "Capacity of the PERSIANN-CDR Product in Detecting Extreme Precipitation over Huai River Basin, China." Remote Sensing 13, no. 9: 1747.

Research article
Published: 13 April 2021 in Marine Geodesy
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Over 70% of the cities in China are experiencing urbanization, and urban heat island intensity (UHII) evaluation studies have been widely performed. However, under the rapid economic development in China, few studies on surface urban heat island (SUHI) interannual variations have been conducted in coastal cities in the leading economic region of the Yangtze River Delta. In this study, the long-term summer daytime SUHI from 2001 to 2019 is studied based on the remotely sensed land surface temperature (LST) in 11 coastal cities in the Yangtze River Delta. The results show that notable SUHIs occur in the study area with high spatial heterogeneity, particularly in the central area, including Shanghai, Hangzhou, and Ningbo. The SUHI trends are not synchronous across the study area, with suburban areas revealing higher trends than city center areas. In addition, all 11 cities show an increasing trend of the urban heat proportion index (UHPI) over 19 years, which is more profound in Shanghai and Zhoushan but less profound in Lianyungang and Wenzhou. The strong correlation between the UHPI and artificial impervious coverage indicates that artificial impervious coverage plays an important role in determining the spatial and temporal distributions of the summer daytime SUHI in the 11 coastal cities, which are especially notable in Ningbo and Taizhou.

ACS Style

Xiao Shi; Yongming Xu; Guojie Wang; Yonghong Liu; Xikun Wei; Xue Hu. Spatiotemporal Variations in the Urban Heat Islands across the Coastal Cities in the Yangtze River Delta, China. Marine Geodesy 2021, 44, 467 -484.

AMA Style

Xiao Shi, Yongming Xu, Guojie Wang, Yonghong Liu, Xikun Wei, Xue Hu. Spatiotemporal Variations in the Urban Heat Islands across the Coastal Cities in the Yangtze River Delta, China. Marine Geodesy. 2021; 44 (5):467-484.

Chicago/Turabian Style

Xiao Shi; Yongming Xu; Guojie Wang; Yonghong Liu; Xikun Wei; Xue Hu. 2021. "Spatiotemporal Variations in the Urban Heat Islands across the Coastal Cities in the Yangtze River Delta, China." Marine Geodesy 44, no. 5: 467-484.

Preprint content
Published: 02 March 2021
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The South Asian High (SAH) location and intensity are linked with the Tibetan Plateau (TP) and Yangtze River basin latent heating. The existing feedbacks of SAH variability is rarely linked with TP soil moisture regulated energy fluxes. In this study, remotely sensed soil moisture and global atmospheric reanalysis products are used to quantify the relationship between the TP spring (April, May, June) soil moisture with SAH and South Asian (SA) monsoon onset during 1988–2008. The diagnostic analysis infers that the SAH exhibits a significant correlation (R ≥ 0.90) with TP spring soil moisture and monsoon onset indices (R≥ -0.56−-0.61). During the early and late monsoon onset, a significant anomalous soil moisture regime influenced the surface energy fluxes, which affected the vertical diabatic heating profile. The diabatic heating profile affects the TP ascending motion and SAH intensity, which leads to regional monsoon circulation changes and onset. An asymmetric SAH movement in the upper troposphere appears before the early and late monsoon onset composites and drives the lower tropospheric westerlies/easterlies winds towards the continental SA. The wind shear and transition from prevailing easterlies into westerlies during the pre-onset, onset, and post-onset pentad results in strong/weak ascent in the Bay of Bengal and advances into continental regions. The onset- mechanism further suggested intensified/weaker westerlies/easterlies during early/late onset composites. The SAH intensity and movement are linked with TP soil moisture, which exhibits teleconnections with the regional circulation pattern. A detailed model experiment will be conducted to verify the influence of soil moisture as a driver of energy fluxes and SA monsoon onset.

ACS Style

Dan Lou; Guojie Wang; Waheed Ullah; Zhiqiu Gao; Asher Samuel Bhatti; Daniel Fiifi Tawia Hagan; Tong Jiang; Buda Su; Chenxia Zhu. The Empirical Influences of Tibetan Plateau Soil Moisture on South Asian Monsoon Onset. 2021, 1 .

AMA Style

Dan Lou, Guojie Wang, Waheed Ullah, Zhiqiu Gao, Asher Samuel Bhatti, Daniel Fiifi Tawia Hagan, Tong Jiang, Buda Su, Chenxia Zhu. The Empirical Influences of Tibetan Plateau Soil Moisture on South Asian Monsoon Onset. . 2021; ():1.

Chicago/Turabian Style

Dan Lou; Guojie Wang; Waheed Ullah; Zhiqiu Gao; Asher Samuel Bhatti; Daniel Fiifi Tawia Hagan; Tong Jiang; Buda Su; Chenxia Zhu. 2021. "The Empirical Influences of Tibetan Plateau Soil Moisture on South Asian Monsoon Onset." , no. : 1.

Research article
Published: 19 February 2021 in International Journal of Climatology
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Projections of future drought conditions under climate change is an important step in formulating the long‐term climate adaptation strategies. It is therefore valuable to predict the drought conditions in China following the release of the CMIP6 (the phase six of the Coupled Model Inter‐comparison Project). Thus, based on 20 global climate model simulations from CMIP6, we project China's drought conditions and its socioeconomic impacts using the self‐calibrated Palmer Drought Severity Index (scPDSI). Four scenarios are considered in this analysis: SSP1‐2.6 (the low‐level development scenario), SSP2‐4.5 (the middle‐level development scenario), SSP3‐7.0 (the medium to high‐level development scenario), and SSP5‐8.5 (the high‐level development scenario). Under SSP1‐2.6, we observed wetting trends over large areas of China except the arid region during 2020‐2099; however, under SSP2‐4.5, SSP3‐7.0 and SSP5‐8.5, significant drying trends are detected in the humid and temperate semi‐humid region, while in other areas there are significant wetting trends. The projected drought conditions are likely to be severe with more frequent monthly occurrences and higher probability of extreme drying conditions, especially in these humid and temperate semi‐humid regions under SSP3‐7.0 and SSP5‐8.5. Consequently, the population exposure to drought in most climatic regions will increase initially up to 2040s and gradually decrease under all the scenarios except SSP3‐7.0; and the humid region will be a future hotspot where the impact of climate on population exposure to drought will be more significant. The economic exposure to drought will increase over the whole China under all 4 scenarios, especially in the humid and semi‐humid region. Our results have important implications for future drought projections and provide a scientific evidence for developing climate change adaptation strategies and disaster prevention.

ACS Style

Liqin Chen; Guojie Wang; Lijuan Miao; Kaushal Raj Gnyawali; Shijie Li; Solomon Obiri Yeboah Amankwah; Jinlong Huang; Jiao Lu; Mingyue Zhan. Future drought in CMIP6 projections and the socioeconomic impacts in China. International Journal of Climatology 2021, 41, 4151 -4170.

AMA Style

Liqin Chen, Guojie Wang, Lijuan Miao, Kaushal Raj Gnyawali, Shijie Li, Solomon Obiri Yeboah Amankwah, Jinlong Huang, Jiao Lu, Mingyue Zhan. Future drought in CMIP6 projections and the socioeconomic impacts in China. International Journal of Climatology. 2021; 41 (8):4151-4170.

Chicago/Turabian Style

Liqin Chen; Guojie Wang; Lijuan Miao; Kaushal Raj Gnyawali; Shijie Li; Solomon Obiri Yeboah Amankwah; Jinlong Huang; Jiao Lu; Mingyue Zhan. 2021. "Future drought in CMIP6 projections and the socioeconomic impacts in China." International Journal of Climatology 41, no. 8: 4151-4170.

Journal article
Published: 02 February 2021 in Remote Sensing
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Drought severity still remains a serious concern across Sub-Saharan Africa (SSA) due to its destructive impact on multiple sectors of society. In this study, the interannual variability and trends in the changes of the self-calibrating Palmer Drought Severity Index (scPDSI) based on the Penman–Monteith (scPDSIPM) and Thornthwaite (scPDSITH) methods for measuring potential evapotranspiration (PET), precipitation (P), normalized difference vegetation index (NDVI), and sea surface temperature (SST) anomalies were investigated through statistical analysis of modeled and remote sensing data. It was shown that scPDSIPM and scPDSITH differed in the representation of drought characteristics over SSA. The regional trend magnitudes of scPDSI in SSA were 0.69 (scPDSIPM) and 0.2 mm/decade (scPDSITH), with a difference in values attributed to the choice of PET measuring method used. The scPDSI and remotely sensed-based anomalies of P and NDVI showed wetting and drying trends over the period 1980–2012 with coefficients of trend magnitudes of 0.12 mm/decade (0.002 mm/decade). The trend analysis showed increased drought events in the semi-arid and arid regions of SSA over the same period. A correlation analysis revealed a strong relationship between the choice of PET measuring method and both P and NDVI anomalies for monsoon and pre-monsoon seasons. The correlation analysis of the choice of PET measuring method with SST anomalies indicated significant positive and negative relationships. This study has demonstrated the applicability of multiple data sources for drought assessment and provides useful information for regional drought predictability and mitigation strategies.

ACS Style

Isaac Kwesi Nooni; Daniel Fiifi T. Hagan; Guojie Wang; Waheed Ullah; Shijie Li; Jiao Lu; Asher Samuel Bhatti; Xiao Shi; Dan Lou; Nana Agyemang Prempeh; Kenny T. C. Lim Kam Sian; Mawuli Dzakpasu; Solomon Obiri Yeboah Amankwah; Chenxia Zhu. Spatiotemporal Characteristics and Trend Analysis of Two Evapotranspiration-Based Drought Products and Their Mechanisms in Sub-Saharan Africa. Remote Sensing 2021, 13, 533 .

AMA Style

Isaac Kwesi Nooni, Daniel Fiifi T. Hagan, Guojie Wang, Waheed Ullah, Shijie Li, Jiao Lu, Asher Samuel Bhatti, Xiao Shi, Dan Lou, Nana Agyemang Prempeh, Kenny T. C. Lim Kam Sian, Mawuli Dzakpasu, Solomon Obiri Yeboah Amankwah, Chenxia Zhu. Spatiotemporal Characteristics and Trend Analysis of Two Evapotranspiration-Based Drought Products and Their Mechanisms in Sub-Saharan Africa. Remote Sensing. 2021; 13 (3):533.

Chicago/Turabian Style

Isaac Kwesi Nooni; Daniel Fiifi T. Hagan; Guojie Wang; Waheed Ullah; Shijie Li; Jiao Lu; Asher Samuel Bhatti; Xiao Shi; Dan Lou; Nana Agyemang Prempeh; Kenny T. C. Lim Kam Sian; Mawuli Dzakpasu; Solomon Obiri Yeboah Amankwah; Chenxia Zhu. 2021. "Spatiotemporal Characteristics and Trend Analysis of Two Evapotranspiration-Based Drought Products and Their Mechanisms in Sub-Saharan Africa." Remote Sensing 13, no. 3: 533.

Journal article
Published: 29 January 2021 in Atmospheric Research
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Deviations in South Asian Summer Monsoon (SASM) precipitation affect the regional floods and drought patterns. In the current study, in-situ observations from Pakistan Meteorological Department (PMD), remotely sensed precipitation data from Climate Hazard Infrared Precipitation with Station data (CHIRPS), reanalysis data from ERA5, and National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) during 1981–2018 are used to explore the atmospheric circulation patterns during above and below-normal precipitation episodes. In a statistical sense, two methods, namely the Empirical Orthogonal Function (EOF) and Percent Normal (PN) indices are used to derive dominant spatial patterns and temporal evolution of extreme monsoon precipitation episodes. Results inferred 60% of the variance to the leading EOF mode depicting a similar spatial pattern of eigenvectors across the region. The leading principal component (PC) and PN index together depicted similar deviations, complementing the extreme flooding and drought years. From the composites, an anomalous increase (decrease) in seasonal precipitation magnitude was observed. The possible mechanism suggests an active control of atmospheric and sea-surface temperature (SST) forcing by altering the wind ascent (descent). The jet streams (200 hPa) intensification of Rossby waves high (low) pressure provides favorable frontal boundaries between polar cold and tropical warm air masses. The westerlies and easterlies are intensified (suppressed) during the above (below) normal precipitation composites, affecting the moisture transport. The enhanced (reduced) convective activities in the Indian Ocean as a primary source affected precipitation in the region during each composite.

ACS Style

Waheed Ullah; Guojie Wang; Dan Lou; Safi Ullah; Asher Samuel Bhatti; Aisha Karim; Daniel Fiifi Tawia Hagan; Gohar Ali. Large-scale atmospheric circulation patterns associated with extreme monsoon precipitation in Pakistan during 1981–2018. Atmospheric Research 2021, 253, 105489 .

AMA Style

Waheed Ullah, Guojie Wang, Dan Lou, Safi Ullah, Asher Samuel Bhatti, Aisha Karim, Daniel Fiifi Tawia Hagan, Gohar Ali. Large-scale atmospheric circulation patterns associated with extreme monsoon precipitation in Pakistan during 1981–2018. Atmospheric Research. 2021; 253 ():105489.

Chicago/Turabian Style

Waheed Ullah; Guojie Wang; Dan Lou; Safi Ullah; Asher Samuel Bhatti; Aisha Karim; Daniel Fiifi Tawia Hagan; Gohar Ali. 2021. "Large-scale atmospheric circulation patterns associated with extreme monsoon precipitation in Pakistan during 1981–2018." Atmospheric Research 253, no. : 105489.

Journal article
Published: 18 January 2021 in Agronomy
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Remote sensing imageries processed through empirical and deterministic approaches help predict multiple agronomic traits throughout the growing season. Accurate identification of cotton crop from remotely sensed imageries is a significant task in precision agriculture. This study aims to utilize a deep learning-based framework for cotton crop field identification with Gaofen-1 (GF-1) high-resolution (16 m) imageries in Wei-Ku region, China. An optimized model for the pixel-wise multidimensional densely connected convolutional neural network (DenseNet) was used. Four widely-used classic convolutional neural networks (CNNs), including ResNet, VGG, SegNet, and DeepLab v3+, were also used for accuracy assessment. The results infer that DenseNet can identify cotton crop features within a relatively shorter time about 5 h for training convergence. The model performance was examined by multiple indicators (P, F1, R, and mIou) produced through the confusion matrix, and the derived cotton fields were then visualized. The DenseNet model has illustrated considerable improvements in comparison with the preceding mainstream models. The results showed that the retrieval precision was 0.948, F1 score was 0.953, and mIou was 0.911. Furthermore, its performance is relatively better in discriminating cotton crop fields’ fine structures when clouds, mountain shadows, and urban built up.

ACS Style

Haolu Li; Guojie Wang; Zhen Dong; Xikun Wei; Mengjuan Wu; Huihui Song; Solomon Obiri Yeboah Amankwah. Identifying Cotton Fields from Remote Sensing Images Using Multiple Deep Learning Networks. Agronomy 2021, 11, 174 .

AMA Style

Haolu Li, Guojie Wang, Zhen Dong, Xikun Wei, Mengjuan Wu, Huihui Song, Solomon Obiri Yeboah Amankwah. Identifying Cotton Fields from Remote Sensing Images Using Multiple Deep Learning Networks. Agronomy. 2021; 11 (1):174.

Chicago/Turabian Style

Haolu Li; Guojie Wang; Zhen Dong; Xikun Wei; Mengjuan Wu; Huihui Song; Solomon Obiri Yeboah Amankwah. 2021. "Identifying Cotton Fields from Remote Sensing Images Using Multiple Deep Learning Networks." Agronomy 11, no. 1: 174.

Research article
Published: 11 January 2021 in International Journal of Climatology
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While air temperature has been widely investigated to better understand the recent climate changes, the importance of soil temperature has been underestimated and rarely studied. Here, the long‐term soil temperature changes and the occurrence of extreme heats have been studied based on daily measurements in Jiangsu Province for 1970–2017. A general warming trend in soil temperature is observed at most stations, and these trends are more pronounced in the cold seasons than the warm seasons. Due to the asymmetric warming of maximum and minimum soil temperatures over the past 48 years, the diurnal range shows a long‐term decreasing trend with a rate of −0.34°C/10a. The occurrences of extreme soil temperatures have been analysed based on the probability density functions. It is found that the hot soil days have increased and cold soil days have decreased significantly, and these changes are particularly significant in the most recent decades.

ACS Style

Xiao Shi; Guojie Wang; Tiexi Chen; Shijie Li; Jiao Lu; Daniel Fiifi T. Hagan. Long‐term changes in layered soil temperature based on ground measurements in Jiangsu Province, China. International Journal of Climatology 2021, 1 .

AMA Style

Xiao Shi, Guojie Wang, Tiexi Chen, Shijie Li, Jiao Lu, Daniel Fiifi T. Hagan. Long‐term changes in layered soil temperature based on ground measurements in Jiangsu Province, China. International Journal of Climatology. 2021; ():1.

Chicago/Turabian Style

Xiao Shi; Guojie Wang; Tiexi Chen; Shijie Li; Jiao Lu; Daniel Fiifi T. Hagan. 2021. "Long‐term changes in layered soil temperature based on ground measurements in Jiangsu Province, China." International Journal of Climatology , no. : 1.

Journal article
Published: 07 September 2020 in Remote Sensing
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Despite numerous assessments of satellite-based and reanalysis precipitation across the globe, few studies have been conducted based on the precipitation linear trend (LT), particularly during daytime and nighttime, when there are different precipitation mechanisms. Herein, we first examine LTs for the whole day (LTwd), daytime (LTd), and nighttime (LTn) over mainland China (MC) in 2003–2017, with sub-daily observations from a dense rain gauge network. For MC and ten Water Resources Regions (WRRs), annual and seasonal LTwd, LTd, and LTn were generally positive but with evident regional differences. Subsequently, annual and seasonal LTs derived from six satellite-based and six reanalysis popular precipitation products were evaluated using metrics of correlation coefficient (CC), bias, root-mean-square-error (RMSE), and sign accuracy. Finally, metric-based optimal products (OPs) were identified for MC and each WRR. Values of each metric for annual and seasonal LTwd, LTd, or LTn differ among products; meanwhile, for any single product, performance varied by season and time of day. Correspondingly, the metric-based OPs varied among regions and seasons, and between daytime and nighttime, but were mainly characterized by OPs of Tropical Rainfall Measuring Mission (TRMM) 3B42, ECMWF Reanalysis (ERA)-Interim, and Modern Era Reanalysis for Research and Applications (MERRA)-2. In particular, the CC-based (RMSE-based) OPs in southern and northern WRRs were generally TRMM3B42 and MERRA-2, respectively. These findings imply that to investigate precipitation change and obtain robust related conclusions using precipitation products, comprehensive evaluations are necessary, due to variation in performance within one year, one day and among regions for different products. Additionally, our study facilitates a valuable reference for product users seeking reliable precipitation estimates to examine precipitation change across MC, and an insight (i.e., capacity in detecting LTs, including daytime and nighttime) for developers improving algorithms.

ACS Style

Shanlei Sun; Wanrong Shi; Shujia Zhou; Rongfan Chai; Haishan Chen; Guojie Wang; Yang Zhou; Huayu Shen. Capacity of Satellite-Based and Reanalysis Precipitation Products in Detecting Long-Term Trends Across Mainland China. Remote Sensing 2020, 12, 2902 .

AMA Style

Shanlei Sun, Wanrong Shi, Shujia Zhou, Rongfan Chai, Haishan Chen, Guojie Wang, Yang Zhou, Huayu Shen. Capacity of Satellite-Based and Reanalysis Precipitation Products in Detecting Long-Term Trends Across Mainland China. Remote Sensing. 2020; 12 (18):2902.

Chicago/Turabian Style

Shanlei Sun; Wanrong Shi; Shujia Zhou; Rongfan Chai; Haishan Chen; Guojie Wang; Yang Zhou; Huayu Shen. 2020. "Capacity of Satellite-Based and Reanalysis Precipitation Products in Detecting Long-Term Trends Across Mainland China." Remote Sensing 12, no. 18: 2902.

Journal article
Published: 07 July 2020 in Remote Sensing
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In this study, an existing combination approach that maximizes temporal correlations is used to combine six passive microwave satellite soil moisture products from 1998 to 2015 to assess its added value in long-term applications. Five of the products used are included in existing merging schemes such as the European Space Agency’s essential climate variable soil moisture (ECV) program. These include the Special Sensor Microwave Imagers (SSM/I), the Tropical Rainfall Measuring Mission (TRMM/TMI), the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) sensor on the National Aeronautics and Space Administration’s (NASA) Aqua satellite, the WindSAT radiometer, onboard the Coriolis satellite and the soil moisture retrievals from the Advanced Microwave Scanning Radiometer 2 (AMSR2) sensor onboard the Global Change Observation Mission on Water (GCOM-W). The sixth, the microwave radiometer imager (MWRI) onboard China’s Fengyun-3B (FY3B) satellite, is absent in the ECV scheme. Here, the normalized soil moisture products are merged based on their availability within the study period. Evaluation of the merged product demonstrated that the correlations and unbiased root mean square differences were improved over the whole period. Compared to ECV, the merged product from this scheme performed better over dense and sparsely vegetated regions. Additionally, the trends in the parent inputs are preserved in the merged data. Further analysis of FY3B’s contribution to the merging scheme showed that it is as dependable as the widely used AMSR2, as it contributed significantly to the improvements in the merged product.

ACS Style

Daniel Hagan; Guojie Wang; Seokhyeon Kim; Robert Parinussa; Yi Liu; Waheed Ullah; Asher Bhatti; Xiaowen Ma; Tong Jiang; Buda Su. Maximizing Temporal Correlations in Long-Term Global Satellite Soil Moisture Data-Merging. Remote Sensing 2020, 12, 2164 .

AMA Style

Daniel Hagan, Guojie Wang, Seokhyeon Kim, Robert Parinussa, Yi Liu, Waheed Ullah, Asher Bhatti, Xiaowen Ma, Tong Jiang, Buda Su. Maximizing Temporal Correlations in Long-Term Global Satellite Soil Moisture Data-Merging. Remote Sensing. 2020; 12 (13):2164.

Chicago/Turabian Style

Daniel Hagan; Guojie Wang; Seokhyeon Kim; Robert Parinussa; Yi Liu; Waheed Ullah; Asher Bhatti; Xiaowen Ma; Tong Jiang; Buda Su. 2020. "Maximizing Temporal Correlations in Long-Term Global Satellite Soil Moisture Data-Merging." Remote Sensing 12, no. 13: 2164.

Journal article
Published: 26 June 2020 in Atmospheric Research
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To characterize future drought events over a drought prone area like South Asia is paramount for drought risk mitigation. In this paper, a five-model ensemble mean from CMIP6 was chosen to project drought characteristics in South Asia under the latest SSPs-RCPs emission scenarios (SSP1–2.6, SSP2–4.5, and SSP5–8.5) for the period 2020–2099. Additionally, corresponding scenarios RCP2.6, RCP4.5 and RCP8.5 of CMIP5 were used for comparison and to identify the changes and improvements of CMIP6 over the South Asia. Principle Component Analysis and the Varimax rotation method is used to divided the study area into five homogenous drought sub-regions. Drought duration, frequency, and intensity are analyzed based on the Run theory, and the Standardized Precipitation Evapotranspiration Index (SPEI) at 12-months timescale, and the self-calibrating Palmer Drought Severity Index (sc-PDSI). The Modified Mann-Kendall and Sen's slope method is adopted to detect sub-regional trends in drought characteristics. Results show that significant increases in drought conditions mainly pronounced over the North-West sub-region. Strong increases are projected in the average drought duration and drought frequency. The North-West sub-region is the most vulnerable to face frequent drought events with longer duration with higher intensity. Parts of the South-West, North-Central, and North-East sub-regions will also face more adverse drought conditions in future. The selected model ensemble from CMIP6 has a very robust capability to simulate present climate parameters (precipitation, temperature, and evaporation) and satisfactorily captures drought characteristics in South Asia. These results provide a basis for developing drought adaptation measures for South Asia.

ACS Style

Jianqing Zhai; Sanjit Kumar Mondal; Thomas Fischer; Yanjun Wang; Buda Su; Jinlong Huang; Hui Tao; Guojie Wang; Waheed Ullah; Jalal Uddin. Future drought characteristics through a multi-model ensemble from CMIP6 over South Asia. Atmospheric Research 2020, 246, 105111 .

AMA Style

Jianqing Zhai, Sanjit Kumar Mondal, Thomas Fischer, Yanjun Wang, Buda Su, Jinlong Huang, Hui Tao, Guojie Wang, Waheed Ullah, Jalal Uddin. Future drought characteristics through a multi-model ensemble from CMIP6 over South Asia. Atmospheric Research. 2020; 246 ():105111.

Chicago/Turabian Style

Jianqing Zhai; Sanjit Kumar Mondal; Thomas Fischer; Yanjun Wang; Buda Su; Jinlong Huang; Hui Tao; Guojie Wang; Waheed Ullah; Jalal Uddin. 2020. "Future drought characteristics through a multi-model ensemble from CMIP6 over South Asia." Atmospheric Research 246, no. : 105111.

Journal article
Published: 01 June 2020 in Remote Sensing
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Fire is a common circumstance in the world. It causes direct casualties and economic losses, and also brings severe negative influences on the atmospheric environment. In the background of climate warming and rising population, it is important to understand the fire responses regarding the spatio-temporal changes. Thus, a long-term change analysis of fires is needed in China. We use the remote sensed MOD14A1/MYD14A1 fire products to analyze the seasonal variations and long-term trends, based on five main land cover types (forest, cropland, grassland, savannas and urban areas). The fires are found to have clear seasonal variations; there are more fires in spring and autumn in vegetated lands, which are related to the amount of dry biomass and temperature. The fire numbers have significantly increased during the study period, especially from spring to autumn, and those have decreased in winter. The long-term fire trends are different when delineated into different land cover types. There are significant increasing fire trends in grasslands and croplands in North, East and Northeast China during the study period. The urban fires also show increasing trends. On the contrary, there are significant decreasing fire trends in forests and savannas in South China where it is most densely vegetated. This study provides an overall analysis of the spatio-temporal fire changes from satellite products, and it may help to understand the fire risk in the changing climate for a better risk management.

ACS Style

Xikun Wei; Guojie Wang; Tiexi Chen; Daniel Fiifi Tawia Hagan; Waheed Ullah. A Spatio-Temporal Analysis of Active Fires over China during 2003–2016. Remote Sensing 2020, 12, 1787 .

AMA Style

Xikun Wei, Guojie Wang, Tiexi Chen, Daniel Fiifi Tawia Hagan, Waheed Ullah. A Spatio-Temporal Analysis of Active Fires over China during 2003–2016. Remote Sensing. 2020; 12 (11):1787.

Chicago/Turabian Style

Xikun Wei; Guojie Wang; Tiexi Chen; Daniel Fiifi Tawia Hagan; Waheed Ullah. 2020. "A Spatio-Temporal Analysis of Active Fires over China during 2003–2016." Remote Sensing 12, no. 11: 1787.

Journal article
Published: 12 March 2020 in Water
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Assessing the long-term precipitation changes is of utmost importance for understanding the impact of climate change. This study investigated the variability of extreme precipitation events over Pakistan on the basis of daily precipitation data from 51 weather stations from 1980-2016. The non-parametric Mann–Kendall, Sen’s slope estimator, least squares method, and two-tailed simple t-test methods were used to assess the trend in eight precipitation extreme indices. These indices were wet days (R1 ≥1 mm), heavy precipitation days (R10 ≥ 10 mm), very heavy precipitation days (R20 ≥ 20 mm), severe precipitation (R50 ≥ 50 mm), very wet days (R95p) defining daily precipitation ≥ 95 percentile, extremely wet days (R99p) defining daily precipitation ≥ 99 percentile, annual total precipitation in wet days (PRCPTOT), and mean precipitation amount on wet days as simple daily intensity index (SDII). The study is unique in terms of using high stations’ density, extended temporal coverage, advanced statistical techniques, and additional extreme indices. Furthermore, this study is the first of its kind to detect abrupt changes in the temporal trend of precipitation extremes over Pakistan. The results showed that the spatial distribution of trends in different precipitation extreme indices over the study region increased as a whole; however, the monsoon and westerlies humid regions experienced a decreasing trend of extreme precipitation indices during the study period. The results of the sequential Mann–Kendall (SqMK) test showed that all precipitation extremes exhibited abrupt dynamic changes in temporal trend during the study period; however, the most frequent mutation points with increasing tendency were observed during 2011 and onward. The results further illustrated that the linear trend of all extreme indices showed an increasing tendency from 1980- 2016. Similarly, for elevation, most of the precipitation extremes showed an inverse relationship, suggesting a decrease of precipitation along the latitudinal extent of the country. The spatiotemporal variations in precipitation extremes give a possible indication of the ongoing phenomena of climate change and variability that modified the precipitation regime of Pakistan. On the basis of the current findings, the study recommends that future studies focus on underlying physical and natural drivers of precipitation variability over the study region.

ACS Style

Asher Samuel Bhatti; Guojie Wang; Waheed Ullah; Safi Ullah; Daniel Fiifi Tawia Hagan; Isaac Kwesi Nooni; Dan Lou; Irfan Ullah. Trend in Extreme Precipitation Indices Based on Long Term In Situ Precipitation Records over Pakistan. Water 2020, 12, 797 .

AMA Style

Asher Samuel Bhatti, Guojie Wang, Waheed Ullah, Safi Ullah, Daniel Fiifi Tawia Hagan, Isaac Kwesi Nooni, Dan Lou, Irfan Ullah. Trend in Extreme Precipitation Indices Based on Long Term In Situ Precipitation Records over Pakistan. Water. 2020; 12 (3):797.

Chicago/Turabian Style

Asher Samuel Bhatti; Guojie Wang; Waheed Ullah; Safi Ullah; Daniel Fiifi Tawia Hagan; Isaac Kwesi Nooni; Dan Lou; Irfan Ullah. 2020. "Trend in Extreme Precipitation Indices Based on Long Term In Situ Precipitation Records over Pakistan." Water 12, no. 3: 797.

Journal article
Published: 02 March 2020 in Remote Sensing
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The accurate acquisition of water information from remote sensing images has become important in water resources monitoring and protections, and flooding disaster assessment. However, there are significant limitations in the traditionally used index for water body identification. In this study, we have proposed a deep convolutional neural network (CNN), based on the multidimensional densely connected convolutional neural network (DenseNet), for identifying water in the Poyang Lake area. The results from DenseNet were compared with the classical convolutional neural networks (CNNs): ResNet, VGG, SegNet and DeepLab v3+, and also compared with the Normalized Difference Water Index (NDWI). Results have indicated that CNNs are superior to the water index method. Among the five CNNs, the proposed DenseNet requires the shortest training time for model convergence, besides DeepLab v3+. The identification accuracies are evaluated through several error metrics. It is shown that the DenseNet performs much better than the other CNNs and the NDWI method considering the precision of identification results; among those, the NDWI performance is by far the poorest. It is suggested that the DenseNet is much better in distinguishing water from clouds and mountain shadows than other CNNs.

ACS Style

Guojie Wang; Mengjuan Wu; Xikun Wei; Huihui Song. Water Identification from High-Resolution Remote Sensing Images Based on Multidimensional Densely Connected Convolutional Neural Networks. Remote Sensing 2020, 12, 795 .

AMA Style

Guojie Wang, Mengjuan Wu, Xikun Wei, Huihui Song. Water Identification from High-Resolution Remote Sensing Images Based on Multidimensional Densely Connected Convolutional Neural Networks. Remote Sensing. 2020; 12 (5):795.

Chicago/Turabian Style

Guojie Wang; Mengjuan Wu; Xikun Wei; Huihui Song. 2020. "Water Identification from High-Resolution Remote Sensing Images Based on Multidimensional Densely Connected Convolutional Neural Networks." Remote Sensing 12, no. 5: 795.

Journal article
Published: 30 December 2019 in Water
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Soil moisture is an important factor in land-atmosphere interactions and other land processes. Improved estimates from climate models have, in the last two decades, become an important alternate source of information. In this study, we extend the evaluation of soil moisture anomalies of different generations of three families of model datasets (the European Center for Medium-Range Weather Forecasts’ (ECMWF) reanalysis, the Modern Era Retrospective Analysis for Research and Applications of NASA, and the Global Land Data Assimilation System of theNational Oceanic and Atmospheric Administration (NOAA)) in recent studies to the People’s Republic of China. Two validation techniques, namely, root-mean-square error (RMSE) from triple collocation analysis (TCA) and correlations (R) with ground observations, were used. The study confirmed the results of previous studies that focused on other regions and showed that the newer generations of each modeling family generally had better skill than the older generations with higher correlations and lower RMSEs. A cross-validation of the results from the two techniques for the newer products showed that the higher correlations and lower RMSEs from the TCA were found over regions with moderate vegetation cover, while regions with less vegetation cover had lower correlations and larger RMSEs (ECMWF (R: −0.93), NASA (R: −0.73), and NOAA (R: −0.61)), indicating that these two techniques complement each other to fairly validate the products.

ACS Style

Daniel Fiifi Tawia Hagan; Robert M. Parinussa; Guojie Wang; Clara S. Draper. An Evaluation of Soil Moisture Anomalies from Global Model-Based Datasets over the People’s Republic of China. Water 2019, 12, 117 .

AMA Style

Daniel Fiifi Tawia Hagan, Robert M. Parinussa, Guojie Wang, Clara S. Draper. An Evaluation of Soil Moisture Anomalies from Global Model-Based Datasets over the People’s Republic of China. Water. 2019; 12 (1):117.

Chicago/Turabian Style

Daniel Fiifi Tawia Hagan; Robert M. Parinussa; Guojie Wang; Clara S. Draper. 2019. "An Evaluation of Soil Moisture Anomalies from Global Model-Based Datasets over the People’s Republic of China." Water 12, no. 1: 117.

Journal article
Published: 27 December 2019 in Water
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Extreme hydrometeorological events have far-reaching impacts on our daily life and may occur more frequently with rising global temperatures. The probability of the concurrence of these extreme events in the upper reaches of the river network is of particular importance for the lower reaches, which is referred to as the encounter probability of extreme events, and may have even stronger socio-economic impacts. In this study, the Rao River basin in China is selected as an example to explore the encounter probability and risk of future flood and drought based on the encounter probability model. The reference period was 1971–2000, and the future prediction periods were 2020–2049 and 2070–2099. The calibrated and validated statistical downscaling model (SDSM) was used to generate future daily precipitation and daily mean temperature. The calibrated and validated Xin’anjiang model was used to predict future daily mean streamflow in the basin. In addition, the encounter probability model was established using the joint distribution of occurrence dates and magnitudes of daily mean streamflow to investigate the encounter probabilities of flood and drought under future climate change. Results show that, for flood occurrence dates, the encounter probability during the flood season would decrease in the two future periods while the dates would generally be earlier. For flood magnitudes, the encounter probability of the two tributaries’ floods and the probability of flood at each tributary would decrease (e.g., the encounter probability with the same-frequency of 100-years would reduce by 53% to 95%), which indicates reduced risk of future major floods in the study area. For drought occurrence dates, the encounter probability during the non-flood season would decrease. For drought magnitudes, the encounter probability would decrease (e.g., the encounter probability with the same-frequency of 100-years would reduce by 18% to 33%), even though the probability of future drought at each tributary would increase. Such analyses provide important probabilistic information to help us prepare for the upcoming extreme events.

ACS Style

Mengyang Liu; Yixing Yin; Xieyao Ma; Zengxin Zhang; Guojie Wang; Shenmin Wang; Wang. Encounter Probability and Risk of Flood and Drought under Future Climate Change in the Two Tributaries of the Rao River Basin, China. Water 2019, 12, 104 .

AMA Style

Mengyang Liu, Yixing Yin, Xieyao Ma, Zengxin Zhang, Guojie Wang, Shenmin Wang, Wang. Encounter Probability and Risk of Flood and Drought under Future Climate Change in the Two Tributaries of the Rao River Basin, China. Water. 2019; 12 (1):104.

Chicago/Turabian Style

Mengyang Liu; Yixing Yin; Xieyao Ma; Zengxin Zhang; Guojie Wang; Shenmin Wang; Wang. 2019. "Encounter Probability and Risk of Flood and Drought under Future Climate Change in the Two Tributaries of the Rao River Basin, China." Water 12, no. 1: 104.

Journal article
Published: 26 December 2019 in Atmosphere
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Soil moisture is an important parameter in land surface processes, which can control the surface energy and water budgets and thus affect the air temperature. Studying the coupling between soil moisture and air temperature is of vital importance for forecasting climate change. This study evaluates this coupling over China from 1980–2013 by using an energy-based diagnostic method, which represents the momentum, heat, and water conservation equations in the atmosphere, while the contributions of soil moisture are treated as external forcing. The results showed that the soil moisture–temperature coupling is strongest in the transitional climate zones between wet and dry climates, which here includes Northeast China and part of the Tibetan Plateau from a viewpoint of annual average. Furthermore, the soil moisture–temperature coupling was found to be stronger in spring than in the other seasons over China, and over different typical climatic zones, it also varied greatly in different seasons. We conducted two case studies (the heatwaves of 2013 in Southeast China and 2009 in North China) to understand the impact of soil moisture–temperature coupling during heatwaves. The results indicated that over areas with soil moisture deficit and temperature anomalies, the coupling strength intensified. This suggests that soil moisture deficits could lead to enhanced heat anomalies, and thus, result in enhanced soil moisture coupling with temperature. This demonstrates the importance of soil moisture and the need to thoroughly study it and its role within the land–atmosphere interaction and the climate on the whole.

ACS Style

Qing Yuan; Guojie Wang; Chenxia Zhu; Dan Lou; Daniel Fiifi Tawia Hagan; Xiaowen Ma; Mingyue Zhan. Coupling of Soil Moisture and Air Temperature from Multiyear Data During 1980–2013 over China. Atmosphere 2019, 11, 25 .

AMA Style

Qing Yuan, Guojie Wang, Chenxia Zhu, Dan Lou, Daniel Fiifi Tawia Hagan, Xiaowen Ma, Mingyue Zhan. Coupling of Soil Moisture and Air Temperature from Multiyear Data During 1980–2013 over China. Atmosphere. 2019; 11 (1):25.

Chicago/Turabian Style

Qing Yuan; Guojie Wang; Chenxia Zhu; Dan Lou; Daniel Fiifi Tawia Hagan; Xiaowen Ma; Mingyue Zhan. 2019. "Coupling of Soil Moisture and Air Temperature from Multiyear Data During 1980–2013 over China." Atmosphere 11, no. 1: 25.