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Zeqiang Chen
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China

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Journal article
Published: 25 November 2020 in Remote Sensing
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Accurate precipitation data at high spatiotemporal resolution are critical for land and water management at the basin scale. We proposed a downscaling framework for Tropical Rainfall Measuring Mission (TRMM) precipitation products through integrating Google Earth Engine (GEE) and Google Colaboratory (Colab). Three machine learning methods, including Gradient Boosting Regressor (GBR), Support Vector Regressor (SVR), and Artificial Neural Network (ANN) were compared in the framework. Three vegetation indices (Normalized Difference Vegetation Index, NDVI; Enhanced Vegetation Index, EVI; Leaf Area Index, LAI), topography, and geolocation are selected as geospatial predictors to perform the downscaling. This framework can automatically optimize the models’ parameters, estimate features’ importance, and downscale the TRMM product to 1 km. The spatial downscaling of TRMM from 25 km to 1 km was achieved by using the relationships between annual precipitations and annually-averaged vegetation index. The monthly precipitation maps derived from the annual downscaled precipitation by disaggregation. According to validation in the Great Mekong upstream region, the ANN yielded the best performance when simulating the annual TRMM precipitation. The most sensitive vegetation index for downscaling TRMM was LAI, followed by EVI. Compared with existing downscaling methods, the proposed framework for downscaling TRMM can be performed online for any given region using a wide range of machine learning tools and environmental variables to generate a precipitation product with high spatiotemporal resolution.

ACS Style

Abdelrazek Elnashar; Hongwei Zeng; Bingfang Wu; Ning Zhang; Fuyou Tian; Miao Zhang; Weiwei Zhu; Nana Yan; Zeqiang Chen; Zhiyu Sun; Xinghua Wu; Yuan Li. Downscaling TRMM Monthly Precipitation Using Google Earth Engine and Google Cloud Computing. Remote Sensing 2020, 12, 3860 .

AMA Style

Abdelrazek Elnashar, Hongwei Zeng, Bingfang Wu, Ning Zhang, Fuyou Tian, Miao Zhang, Weiwei Zhu, Nana Yan, Zeqiang Chen, Zhiyu Sun, Xinghua Wu, Yuan Li. Downscaling TRMM Monthly Precipitation Using Google Earth Engine and Google Cloud Computing. Remote Sensing. 2020; 12 (23):3860.

Chicago/Turabian Style

Abdelrazek Elnashar; Hongwei Zeng; Bingfang Wu; Ning Zhang; Fuyou Tian; Miao Zhang; Weiwei Zhu; Nana Yan; Zeqiang Chen; Zhiyu Sun; Xinghua Wu; Yuan Li. 2020. "Downscaling TRMM Monthly Precipitation Using Google Earth Engine and Google Cloud Computing." Remote Sensing 12, no. 23: 3860.

Research article
Published: 25 November 2020 in Journal of Sensors
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Along with the rapid development of remote sensing satellites and sensor network technology, vast amounts of remote sensing imagery and in situ observations have been accumulated. Further, various researchers and agencies have released a variety of thematic image products. These heterogeneous observations are therefore difficult to utilize comprehensively. In this study, an ontology-based framework for integrating remote sensing imagery, image products, and in situ observations was developed. It was extended based on the Semantic Sensor Network (SSN) ontology in the Web Ontology Language (OWL). The detailed process of ontology construction and rule establishment was demonstrated. Combined with some actual remote sensing imagery, image products, and in situ observations, semantic queries based on DL Query and SPARQL were conducted to establish the rationality and feasibility of the ontology and framework.

ACS Style

Chao Wang; Xinyan Zhuo; Pengfei Li; Nengcheng Chen; Wei Wang; Zeqiang Chen. An Ontology-Based Framework for Integrating Remote Sensing Imagery, Image Products, and In Situ Observations. Journal of Sensors 2020, 2020, 1 -12.

AMA Style

Chao Wang, Xinyan Zhuo, Pengfei Li, Nengcheng Chen, Wei Wang, Zeqiang Chen. An Ontology-Based Framework for Integrating Remote Sensing Imagery, Image Products, and In Situ Observations. Journal of Sensors. 2020; 2020 ():1-12.

Chicago/Turabian Style

Chao Wang; Xinyan Zhuo; Pengfei Li; Nengcheng Chen; Wei Wang; Zeqiang Chen. 2020. "An Ontology-Based Framework for Integrating Remote Sensing Imagery, Image Products, and In Situ Observations." Journal of Sensors 2020, no. : 1-12.

Journal article
Published: 24 September 2020 in Atmospheric Research
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Precipitation products are good choices to complement ground observations in hydrological research, but their accuracy is uncertain in different areas. This study aims to evaluate the systematic error characteristics of four major precipitation products, namely, Climate Prediction Center MORPHing technique(CMORPH), Climate Hazards Group Infrared Precipitation with Stations (CHIRPS), Global Land Data Assimilation System (GLDAS), and Tropical Rainfall Measuring Mission(TRMM) 3B42 v7, over the Yangtze River Basin in terms of estimating precipitation amounts and detecting events. The results show that the precipitation products have high spatial and seasonal heterogeneity of error characteristics, and the capability to capture rain occurrence decreases when rainfall intensity increases. GLDAS demonstrated the poorest performance, with the lowest correlation of 0.08 and the largest relative bias of over 25% underestimation. The possibility of GLDAS missing medium and heavy rains (>15 mm/d) reached 50%, and of falsely reporting light rainfall was up to 40%, while CMORPH outperformed the others with the highest consistency (0.39) against the gauge, the smallest root-mean-square error (RMSE) (10.28 mm), and the highest scores for most subregions. Generally, in this study GLDAS proved its inferiority to satellite-based precipitation products for hydrological applications over the Yangtze River Basin.

ACS Style

Wei Wang; Hui Lin; Nengcheng Chen; Zeqiang Chen. Evaluation of multi-source precipitation products over the Yangtze River Basin. Atmospheric Research 2020, 249, 105287 .

AMA Style

Wei Wang, Hui Lin, Nengcheng Chen, Zeqiang Chen. Evaluation of multi-source precipitation products over the Yangtze River Basin. Atmospheric Research. 2020; 249 ():105287.

Chicago/Turabian Style

Wei Wang; Hui Lin; Nengcheng Chen; Zeqiang Chen. 2020. "Evaluation of multi-source precipitation products over the Yangtze River Basin." Atmospheric Research 249, no. : 105287.

Articles
Published: 24 August 2020 in International Journal of Digital Earth
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Long-term and large-scale lake statistics are meaningful for the study of environment change, but many of the existing methods are labour-intensive and time-consuming. To overcome this problem, a novel method for long-term and large-scale lake extraction by shape-factors- and machine-learning-based water body classification is proposed. An experiment was conducted to extract the lakes in the Yangtze River basin (YRB) from 2008 to 2018 with the Joint Research Centre's Global Surface Water Dataset (JRC GSW) data and OSM data. The results show: 1) The proposed method is automatically and successfully executed. 2) The number of river–lake complexes is between 3008 and 4697, representing 3.56%–5.70% of the total water bodies. 3) The areas of the lakes and rivers in the YRB were obtained, and the accuracy of water classification in each year was stable between 90.2% and 93.6%. Comparing the back propagation neural network, random forest, and support vector machine models, we found that the three machine learning models have similar classification accuracy for the scenario. 4) Fragmented and incomplete small rivers in the JRC GSW data, unchecked training samples, and overlapped shape factors are the three error sources. Future work will focus on addressing these three error sources.

ACS Style

Jin Luo; Zeqiang Chen; Nengcheng Chen. Distinguishing different subclasses of water bodies for long-term and large-scale statistics of lakes: a case study of the Yangtze River basin from 2008 to 2018. International Journal of Digital Earth 2020, 14, 202 -230.

AMA Style

Jin Luo, Zeqiang Chen, Nengcheng Chen. Distinguishing different subclasses of water bodies for long-term and large-scale statistics of lakes: a case study of the Yangtze River basin from 2008 to 2018. International Journal of Digital Earth. 2020; 14 (2):202-230.

Chicago/Turabian Style

Jin Luo; Zeqiang Chen; Nengcheng Chen. 2020. "Distinguishing different subclasses of water bodies for long-term and large-scale statistics of lakes: a case study of the Yangtze River basin from 2008 to 2018." International Journal of Digital Earth 14, no. 2: 202-230.

Journal article
Published: 04 June 2020 in Remote Sensing
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Tea is a cash crop that improves the quality of life for people in the Tanuyen District of Laichau Province, Vietnam. Tea yield, however, has stagnated in recent years, due to changes in temperature, precipitation, the age of the tea bushes, and diseases. Developing an approach for monitoring tea bushes by remote sensing and Geographic Information Systems (GIS) might be a way to alleviate this problem. Using multi-temporal remote sensing data, the paper details an investigation of the changes in tea health and yield forecasting through the normalized difference vegetation index (NDVI). In this study, we used NDVI as a support tool to demonstrate the temporal and spatial changes in NDVI through the extract tea NDVI value and calculate the mean NDVI value. The results of the study showed that the minimum NDVI value was 0.42 during January 2013 and February 2015 and 2016. The maximum NDVI value was in August 2015 and June 2017. We indicate that the linear relationship between NDVI value and mean temperature was strong with R 2 = 0.79 Our results confirm that the combination of meteorological data and NDVI data can achieve a high performance of yield prediction. Three models to predict tea yield were conducted: support vector machine (SVM), random forest (RF), and the traditional linear regression model (TLRM). For period 2009 to 2018, the prediction tea yield by the RF model was the best with a R 2 = 0.73 , by SVM it was 0.66, and 0.57 with the TLRM. Three evaluation indicators were used to consider accuracy: the coefficient of determination ( R 2 ), root-mean-square error (RMSE), and percentage error of tea yield (PETY). The highest accuracy for the three models was in 2015 with a R 2 ≥ 0.87, RMSE < 50 kg/ha, and PETY less 3% error. In the other years, the prediction accuracy was higher in the SVM and RF models. Meanwhile, the RF algorithm was better than PETY (≤10%) and the root mean square error for this algorithm was significantly less (≤80 kg/ha). RMSE and PETY showed relatively good values in the TLRM model with a RMSE from 80 to 100 kg/ha and a PETY from 8 to 15%.

ACS Style

Phamchimai Phan; Nengcheng Chen; Lei Xu; Zeqiang Chen. Using Multi-Temporal MODIS NDVI Data to Monitor Tea Status and Forecast Yield: A Case Study at Tanuyen, Laichau, Vietnam. Remote Sensing 2020, 12, 1814 .

AMA Style

Phamchimai Phan, Nengcheng Chen, Lei Xu, Zeqiang Chen. Using Multi-Temporal MODIS NDVI Data to Monitor Tea Status and Forecast Yield: A Case Study at Tanuyen, Laichau, Vietnam. Remote Sensing. 2020; 12 (11):1814.

Chicago/Turabian Style

Phamchimai Phan; Nengcheng Chen; Lei Xu; Zeqiang Chen. 2020. "Using Multi-Temporal MODIS NDVI Data to Monitor Tea Status and Forecast Yield: A Case Study at Tanuyen, Laichau, Vietnam." Remote Sensing 12, no. 11: 1814.

Journal article
Published: 29 March 2020 in Remote Sensing
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Modeling the relationship between precipitation and water level is of great significance in the prevention of flood disaster. In recent years, the use of machine learning algorithms for precipitation–water level prediction has attracted wide attention in flood forecasting and other fields; however, a clear method to model the relationship of precipitation and water level using grid precipitation products with a neural network model is lacking. The issues of the method include how to select a neural network model, as well as how to influence the modeling results with different types and resolutions of remote sensing data. The purpose of this paper is to provide some findings for the issues. We used the back-propagation (BP) neural network and a nonlinear autoregressive exogenous model (NARX) time series network to model the relationship between precipitation and water level, respectively. The water level of Pingshan hydrographic station at a catchment area in the Jinsha River Basin was simulated by the two network models using three different grid precipitation products. The results showed that when the ground station data are missing, the grid precipitation product is a good alternative to construct the precipitation–water level relationship. In addition, using the NARX network as a model fitting network using extra inputs was better than using the BP neural network; the Nash efficiency coefficients of the former were all higher than 97%, while the latter were all lower than 94%. Furthermore, the input of grid products with different spatial resolutions has little significant effect on the modeling results of the model.

ACS Style

Zeqiang Chen; Xin Lin; Chang Xiong; Nengcheng Chen. Modeling the Relationship of Precipitation and Water Level Using Grid Precipitation Products with a Neural Network Model. Remote Sensing 2020, 12, 1096 .

AMA Style

Zeqiang Chen, Xin Lin, Chang Xiong, Nengcheng Chen. Modeling the Relationship of Precipitation and Water Level Using Grid Precipitation Products with a Neural Network Model. Remote Sensing. 2020; 12 (7):1096.

Chicago/Turabian Style

Zeqiang Chen; Xin Lin; Chang Xiong; Nengcheng Chen. 2020. "Modeling the Relationship of Precipitation and Water Level Using Grid Precipitation Products with a Neural Network Model." Remote Sensing 12, no. 7: 1096.

Research article
Published: 09 March 2020 in Advances in Meteorology
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Downscaling considerably alleviates the drawbacks of regional climate simulation by general circulation models (GCMs). However, little information is available regarding the downscaling using machine learning methods, specifically at hydrological basin scale. This study developed multiple machine learning (ML) downscaling models, based on a Bayesian model average (BMA), to downscale the precipitation simulation of 8 Coupled Model Intercomparison Project Phase 5 (CMIP5) models using model output statistics (MOS) for the years 1961–2005 in the upper Han River basin. A series of statistical metrics, including Pearson’s correlation coefficient (PCC), root mean squared error (RMSE), and relative bias (Rbias), were used for evaluation and comparative analyses. Moreover, the BMA and the best ML downscaling model were used to downscale precipitation in the 21st century under Representative Concentration Pathway 4.5 (RCP4.5) and RCP8.5 scenarios. The results show the following: (1) The performance of the BMA ensemble simulation is clearly better than that of the individual models and the simple mean model ensemble (MME). The PCC reaches 0.74, and the RMSE is reduced by 28%–60% for all the GCMs and 33% compared to the MME. (2) The downscaled models greatly improved station simulation performance. Support vector machine for regression (SVR) was superior to multilayer perceptron (MLP) and random forest (RF). The downscaling results based on the BMA ensemble simulation and SVR models were regarded as the best performing overall (PCC, RMSE, and Rbias were 0.82, 35.07, mm and −5.45%, respectively). (3) Based on BMA and SVR models, the projected precipitations show a weak increasing trend on the whole under RCP4.5 and RCP8.5. Specifically, the average rainfall during the mid- (2040–2069) and late (2070–2099) 21st century increased by 3.23% and 1.02%, respectively, compared to the base year (1971–2000) under RCP4.5, while they increased by 4.25% and 8.30% under RCP8.5. Additionally, the magnitude of changes during winter and spring was higher than that during summer and autumn. Furthermore, future work is recommended to study the improvement of downscaling models and the effect of local climate.

ACS Style

Ren Xu; Nengcheng Chen; Yumin Chen; Zeqiang Chen. Downscaling and Projection of Multi-CMIP5 Precipitation Using Machine Learning Methods in the Upper Han River Basin. Advances in Meteorology 2020, 2020, 1 -17.

AMA Style

Ren Xu, Nengcheng Chen, Yumin Chen, Zeqiang Chen. Downscaling and Projection of Multi-CMIP5 Precipitation Using Machine Learning Methods in the Upper Han River Basin. Advances in Meteorology. 2020; 2020 ():1-17.

Chicago/Turabian Style

Ren Xu; Nengcheng Chen; Yumin Chen; Zeqiang Chen. 2020. "Downscaling and Projection of Multi-CMIP5 Precipitation Using Machine Learning Methods in the Upper Han River Basin." Advances in Meteorology 2020, no. : 1-17.

Journal article
Published: 28 November 2019 in ISPRS International Journal of Geo-Information
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Commonly, a three-dimensional (3D) geographic information system (GIS) is based on a two-dimensional (2D) visualization platform, hindering the understanding and expression of the real world in 3D space that further limits user cognition and understanding of 3D geographic information. Mixed reality (MR) adopts 3D display technology, which enables users to recognize and understand a computer-generated world from the perspective of 3D glasses and solves the problem that users are restricted to the perspective of a 2D screen, with a broad application foreground. However, there is a gap, especially dynamically, in modelling and visualizing a holographic 3D geographical Scene with GIS data/information under the development mechanism of a mixed reality system (e.g., the Microsoft HoloLens). This paper attempts to propose a design architecture (HoloDym3DGeoSce) to model and visualize holographic 3D geographical scenes with timely data based on mixed reality technology and the Microsoft HoloLens. The HoloDym3DGeoSce includes two modules, 3D geographic scene modelling with timely data and HoloDym3DGeoSce interactive design. 3D geographic scene modelling with timely data dynamically creates 3D geographic scenes based on Web services, providing materials and content for the HoloDym3DGeoSce system. The HoloDym3DGeoSce interaction module includes two methods: Human–computer physical interaction and human–computer virtual–real interaction. The human–computer physical interaction method provides an interface for users to interact with virtual geographic scenes. The human–computer virtual–real interaction method maps virtual geographic scenes to physical space to achieve virtual and real fusion. According to the proposed architecture design scheme, OpenStreetMap data and the BingMap Server are used as experimental data to realize the application of mixed reality technology to the modelling, rendering, and interacting of 3D geographic scenes, providing users with a stronger and more realistic 3D geographic information experience, and more natural human–computer GIS interactions. The experimental results show that the feasibility and practicability of the scheme have good prospects for further development.

ACS Style

Wei Wang; Xingxing Wu; An He; Zeqiang Chen. Modelling and Visualizing Holographic 3D Geographical Scenes with Timely Data Based on the HoloLens. ISPRS International Journal of Geo-Information 2019, 8, 539 .

AMA Style

Wei Wang, Xingxing Wu, An He, Zeqiang Chen. Modelling and Visualizing Holographic 3D Geographical Scenes with Timely Data Based on the HoloLens. ISPRS International Journal of Geo-Information. 2019; 8 (12):539.

Chicago/Turabian Style

Wei Wang; Xingxing Wu; An He; Zeqiang Chen. 2019. "Modelling and Visualizing Holographic 3D Geographical Scenes with Timely Data Based on the HoloLens." ISPRS International Journal of Geo-Information 8, no. 12: 539.

Journal article
Published: 26 November 2019 in Journal of Geophysical Research: Atmospheres
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Terrestrial water storage (TWS) changes are driven by internal climate variability, external natural climate change, external human‐caused climate change, and human water management. Spatiotemporal patterns of TWS change and attribution can help understand the water cycle process and refine water management in a region. Here, the spatiotemporal changes in the TWS in China between 2003 and 2016 are analyzed based on three monthly mass concentration (mascon) data products from the Gravity Recovery and Climate Experiment (GRACE) satellites, and the possible drivers are investigated using multiple precipitation products, land surface models (LSMs), multisource remote sensing and socioeconomic data. Six major TWS change regions are detected, including negative trends in northwestern China (NWC), the southeastern Tibetan Plateau (SETP) and northern China (NC), and positive trends in western China (WC), southern China (SC) and northeastern China (NEC). Two global hydrological models (GHMs), WaterGAP and PCR‐GLOBWB, substantially underestimate the TWS changes relative to GRACE. Four LSMs (CLM 2.0, VIC, MOSAIC and NOAH 3.3) from the Global Land Data Assimilation System (GLDAS) version 2.1 show large model uncertainties in simulating the TWS trend. A statistical attribution indicates that ice melting under human‐caused climate change is a driver of decreasing TWS in NWC and the SETP that cannot be ignored, while human water use is largely responsible for groundwater depletion in NC. The increasing TWS in SC and NEC is likely caused by precipitation increases and the increasing TWS in WC is probably a result of precipitation increases and water supplementation from ice melting.

ACS Style

Lei Xu; Nengcheng Chen; Xiang Zhang; Zeqiang Chen. Spatiotemporal Changes in China's Terrestrial Water Storage From GRACE Satellites and Its Possible Drivers. Journal of Geophysical Research: Atmospheres 2019, 124, 11976 -11993.

AMA Style

Lei Xu, Nengcheng Chen, Xiang Zhang, Zeqiang Chen. Spatiotemporal Changes in China's Terrestrial Water Storage From GRACE Satellites and Its Possible Drivers. Journal of Geophysical Research: Atmospheres. 2019; 124 (22):11976-11993.

Chicago/Turabian Style

Lei Xu; Nengcheng Chen; Xiang Zhang; Zeqiang Chen. 2019. "Spatiotemporal Changes in China's Terrestrial Water Storage From GRACE Satellites and Its Possible Drivers." Journal of Geophysical Research: Atmospheres 124, no. 22: 11976-11993.

Journal article
Published: 21 November 2019 in Earth System Science Data
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Accurate and consistent satellite-based precipitation estimates blended with rain gauge data are important for regional precipitation monitoring and hydrological applications, especially in regions with limited rain gauges. However, the existing fusion precipitation estimates often have large uncertainties over mountainous areas with complex topography and sparse rain gauges, and most of the existing data blending algorithms are not good at removing the day-by-day errors. Therefore, the development of effective methods for high-accuracy precipitation estimates over complex terrain and at a daily scale is of vital importance for mountainous hydrological applications. This study aims to offer a novel approach for blending daily precipitation gauge data and the Climate Hazards Group Infrared Precipitation (CHIRP; daily, 0.05∘) satellite-derived precipitation developed by UC Santa Barbara over the Jinsha River basin from 1994 to 2014. This method is called the Wuhan University Satellite and Gauge precipitation Collaborated Correction (WHU-SGCC). The results show that the WHU-SGCC method is effective for liquid precipitation bias adjustments from points to surfaces as evaluated by multiple error statistics and from different perspectives. Compared with CHIRP and CHIRP with station data (CHIRPS), the precipitation adjusted by the WHU-SGCC method has greater accuracy, with overall average improvements of the Pearson correlation coefficient (PCC) by 0.0082–0.2232 and 0.0612–0.3243, respectively, and decreases in the root mean square error (RMSE) by 0.0922–0.65 and 0.2249–2.9525 mm, respectively. In addition, the Nash–Sutcliffe efficiency coefficient (NSE) of the WHU-SGCC provides more substantial improvements than CHIRP and CHIRPS, which reached 0.2836, 0.2944, and 0.1853 in the spring, autumn, and winter. Daily accuracy evaluations indicate that the WHU-SGCC method has the best ability to reduce precipitation bias, with average reductions of 21.68 % and 31.44 % compared to CHIRP and CHIRPS, respectively. Moreover, the accuracy of the spatial distribution of the precipitation estimates derived from the WHU-SGCC method is related to the complexity of the topography. The validation also verifies that the proposed approach is effective at detecting major precipitation events within the Jinsha River basin. In spite of the correction, the uncertainties in the seasonal precipitation forecasts in the summer and winter are still large, which might be due to the homogenization attenuating the extreme rain event estimates. However, the WHU-SGCC approach may serve as a promising tool to monitor daily precipitation over the Jinsha River basin, which contains complicated mountainous terrain with sparse rain gauge data, based on the spatial correlation and the historical precipitation characteristics. The daily precipitation estimations at the 0.05∘ resolution over the Jinsha River basin during all four seasons from 1990 to 2014, derived from WHU-SGCC, are available at the PANGAEA Data Publisher for Earth & Environmental Science portal (https://doi.org/10.1594/PANGAEA.905376, Shen et al., 2019).

ACS Style

Gaoyun Shen; Nengcheng Chen; Wei Wang; Zeqiang Chen. WHU-SGCC: a novel approach for blending daily satellite (CHIRP) and precipitation observations over the Jinsha River basin. Earth System Science Data 2019, 11, 1711 -1744.

AMA Style

Gaoyun Shen, Nengcheng Chen, Wei Wang, Zeqiang Chen. WHU-SGCC: a novel approach for blending daily satellite (CHIRP) and precipitation observations over the Jinsha River basin. Earth System Science Data. 2019; 11 (4):1711-1744.

Chicago/Turabian Style

Gaoyun Shen; Nengcheng Chen; Wei Wang; Zeqiang Chen. 2019. "WHU-SGCC: a novel approach for blending daily satellite (CHIRP) and precipitation observations over the Jinsha River basin." Earth System Science Data 11, no. 4: 1711-1744.

Journal article
Published: 08 November 2019 in Atmosphere
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After the release of the high-resolution downscaled National Aeronautics and Space Administration (NASA) Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset, it is worth exploiting this dataset to improve the simulation and projection of local precipitation. This study developed support vector regression (SVR) and quantile mapping (SVR_QM) ensemble and correction models on the basis of historic precipitation in the Han River basin and the 21 NEX-GDDP models. The generated SVR_QM models were applied to project changes of precipitation during the 21st century for the region. Several statistical metrics, including Pearson’s correlation coefficient (PCC), root mean squared error (RMSE), and relative bias (Rbias), were used for evaluation and comparative analyses. The results demonstrated the superior performance of SVR_QM compared with multi-layer perceptron (MLP), SVR, and random forest (RF), as well as simple model average (MME) ensemble methods and single NEX-GDDP models. PCC was up to 0.84 from 0.61–0.71 for the single NEX-GDDP models, RMSE was up to 34.02 mm from 48–51 mm, and Rbias values were almost removed. Additionally, the projected precipitation changes during the 21st century in most stations had an increasing trend under both Representative Concentration Pathway RCP4.5 and RCP8.5 emissions scenarios; the regional average precipitation during the middle (2040–2059) and late (2070–2089) 21st century increased by 3.54% and 5.12% under RCP4.5 and by 7.44% and 9.52% under RCP8.5, respectively.

ACS Style

Ren Xu; Yumin Chen; Zeqiang Chen. Future Changes of Precipitation over the Han River Basin Using NEX-GDDP Dataset and the SVR_QM Method. Atmosphere 2019, 10, 688 .

AMA Style

Ren Xu, Yumin Chen, Zeqiang Chen. Future Changes of Precipitation over the Han River Basin Using NEX-GDDP Dataset and the SVR_QM Method. Atmosphere. 2019; 10 (11):688.

Chicago/Turabian Style

Ren Xu; Yumin Chen; Zeqiang Chen. 2019. "Future Changes of Precipitation over the Han River Basin Using NEX-GDDP Dataset and the SVR_QM Method." Atmosphere 10, no. 11: 688.

Journal article
Published: 29 August 2019 in Water
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Accurate water-level prediction is of great significance to flood disaster monitoring. A genetic algorithm coupling a back-propagation neural network (GA-BPNN) has been adopted as a hybrid model to improve forecast performance. However, a traditional genetic algorithm can easily to fall into locally limited optimization and local convergence when facing a complex neural network. To deal with this problem, a novel method called an improved genetic algorithm (IGA) coupling a back-propagation neural network model (IGA-BPNN) is proposed with a variety of genetic strategies. The strategies are to supply a genetic population by a chaotic sequence, multi-type genetic strategies, adaptive dynamic probability adjustment and an attenuated genetic strategy. An experiment was tested to predict the water level in the middle and lower reaches of the Han River, China, with meteorological and hydrological data from 2010 to 2017. In the experiment, the IGA-BPNN, traditional GA-BPNN and an artificial neural network (ANN) were evaluated and compared using the root mean square error (RMSE), Nash–Sutcliffe efficiency (NSE) coefficient and Pearson correlation coefficient (R) as the key indicators. The results showed that IGA-BPNN moderately correlates with the observed water level, outperforming the other two models on three indicators. The IGA-BPNN model can settle problems including the limited optimization effect and local convergence; it also improves the prediction accuracy and the model stability regardless of the scenario, i.e., sudden floods or a period of less rainfall.

ACS Style

Nengcheng Chen; Chang Xiong; Wenying Du; Chao Wang; Xin Lin; Zeqiang Chen. An Improved Genetic Algorithm Coupling a Back-Propagation Neural Network Model (IGA-BPNN) for Water-Level Predictions. Water 2019, 11, 1795 .

AMA Style

Nengcheng Chen, Chang Xiong, Wenying Du, Chao Wang, Xin Lin, Zeqiang Chen. An Improved Genetic Algorithm Coupling a Back-Propagation Neural Network Model (IGA-BPNN) for Water-Level Predictions. Water. 2019; 11 (9):1795.

Chicago/Turabian Style

Nengcheng Chen; Chang Xiong; Wenying Du; Chao Wang; Xin Lin; Zeqiang Chen. 2019. "An Improved Genetic Algorithm Coupling a Back-Propagation Neural Network Model (IGA-BPNN) for Water-Level Predictions." Water 11, no. 9: 1795.

Journal article
Published: 14 August 2019 in Remote Sensing of Environment
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Sea surface temperature (SST) is one of the most important parameters in the global ocean-atmospheric system, changes of which can have profound effects on the global climate and may lead to extreme weather events such as droughts and floods. Therefore, predicting the dynamics of future SSTs is of vital importance which can help identify these extreme events and alleviate the losses they cause. In this paper, a machine learning method combining the long short-term memory (LSTM) deep recurrent neural network model and the AdaBoost ensemble learning model (LSTM-AdaBoost) is proposed to predict the short and mid-term daily SST considering that LSTM is good at modelling long-term dependencies but suffers from overfitting, while AdaBoost has strong prediction capability and is not easily overfitted. By combining these two strong and heterogeneous models, the prediction errors related to variance may cancel out each other and the final results can be improved. In this method, the historical time-series satellite data of SST anomaly (SSTA) is used instead of SST itself considering that the fluctuations of SSTs are very small compared to their absolute magnitudes. The seasonality of the SSTA time series is first modelled using polynomial regression and then removed. Then, the deseasonalized time series are used to train the developed LSTM model and AdaBoost model independently. Daily SSTA predictions are made using these two models, and eventually, their predictions are combined as final predictions using the averaging strategy. A case study in the East China Sea that predicts the daily SSTA 10 days ahead shows that the proposed LSTM-AdaBoost combination model outperforms the LSTM and AdaBoost separately, as well as the optimized support vector regression (SVR) model, the optimized feedforward backpropagation neural network model (BPNN), and the stacking LSTM-AdaBoost model (S_LSTM-AdaBoost), when judged using multiple error statistics and from different perspectives. The results suggest that the LSTM-AdaBoost combination model using the averaging strategy is highly promising for short and mid-term daily SST predictions.

ACS Style

Changjiang Xiao; Nengcheng Chen; Chuli Hu; Ke Wang; Jianya Gong; Zeqiang Chen. Short and mid-term sea surface temperature prediction using time-series satellite data and LSTM-AdaBoost combination approach. Remote Sensing of Environment 2019, 233, 111358 .

AMA Style

Changjiang Xiao, Nengcheng Chen, Chuli Hu, Ke Wang, Jianya Gong, Zeqiang Chen. Short and mid-term sea surface temperature prediction using time-series satellite data and LSTM-AdaBoost combination approach. Remote Sensing of Environment. 2019; 233 ():111358.

Chicago/Turabian Style

Changjiang Xiao; Nengcheng Chen; Chuli Hu; Ke Wang; Jianya Gong; Zeqiang Chen. 2019. "Short and mid-term sea surface temperature prediction using time-series satellite data and LSTM-AdaBoost combination approach." Remote Sensing of Environment 233, no. : 111358.

Journal article
Published: 04 July 2019 in Remote Sensing
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The real-time flood inundation extent plays an important role in flood disaster preparation and reduction. To date, many approaches have been developed for determining the flood extent, such as hydrodynamic models, digital elevation model-based (DEM-based) methods, and remote sensing methods. However, hydrodynamic methods are time consuming when applied to large floodplains, high-resolution DEMs are not always available, and remote sensing imagery cannot be used alone to predict inundation. In this article, a new model for the highly accurate and rapid simulation of floodplains, called “RFim” (real-time inundation model), is proposed to simulate the real-time flooded area. The model combines remote sensing images with in situ data to find the relationship between the inundation extent and water level. The new approach takes advantage of remote sensing images, which have wide spatial coverage and high resolution, and in situ observations, which have continuous temporal coverage and are easily accessible. This approach has been applied in the study area of East Dongting Lake, representing a large floodplain, for inundation simulation at a 30 m resolution. Compared with the submerged extent from observations, the accuracy of the simulation could be more than 90% (the lowest is 93%, and the highest is 96%). Hence, the approach proposed in this study is reliable for predicting the flood extent. Moreover, an inundation simulation for all of 2013 was performed with daily water level observation data. With an increasing number of Earth observation satellites operating in space and high-resolution mappers deployed on satellites, it will be much easier to acquire large quantities of images with very high resolutions. Therefore, the use of RFim to perform inundation simulations with high accuracy and high spatial resolutions in the future is promising because the simulation model is built on remote sensing imagery and gauging station data.

ACS Style

Zeqiang Chen; Jin Luo; Nengcheng Chen; Ren Xu; Gaoyun Shen. RFim: A Real-Time Inundation Extent Model for Large Floodplains Based on Remote Sensing Big Data and Water Level Observations. Remote Sensing 2019, 11, 1585 .

AMA Style

Zeqiang Chen, Jin Luo, Nengcheng Chen, Ren Xu, Gaoyun Shen. RFim: A Real-Time Inundation Extent Model for Large Floodplains Based on Remote Sensing Big Data and Water Level Observations. Remote Sensing. 2019; 11 (13):1585.

Chicago/Turabian Style

Zeqiang Chen; Jin Luo; Nengcheng Chen; Ren Xu; Gaoyun Shen. 2019. "RFim: A Real-Time Inundation Extent Model for Large Floodplains Based on Remote Sensing Big Data and Water Level Observations." Remote Sensing 11, no. 13: 1585.

Journal article
Published: 28 February 2019 in ISPRS International Journal of Geo-Information
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The timely sharing and interoperation of multi-source cross-sectoral information is an important issue for a Geographic Information System (GIS). To study this issue, a real-time and open GIS model called GeoSensor is proposed in this work. GeoSensor integrates the real-time GIS model, real-time computation framework, and Open Geospatial Consortium services. This paper illustrates the system architecture and the implementation methods of the GeoSensor. One of the methods developed is the conceptual mapping of a real-time GIS data model to open GIS models and services and a real-time computation framework. The other method developed is the integration of open GIS services, a real-time computation framework, and hybrid databases. The GeoSensor was tested in a case study of building a smart river. In the case study, a comprehensive sensor web was constructed and integrated with the real-time information on rainfall, beacon, channel, sediment, and water levels derived from space-based sensors, air-borne sensors, and underground sensors from multiple sectors in the Yangtze River basin. The GeoSensor manages the comprehensive sensor web with 32 types of sensors and more than 10 billion observation records. Three application systems were developed based on the GeoSensor to manage flood control, hydropower production, and navigation of the Yangtze River. The results of the three application systems show that the real-time and open system improves the management efficiency of the Yangtze River. This system is promising for managing smart rivers.

ACS Style

Zeqiang Chen; Nengcheng Chen. A Real-Time and Open Geographic Information System and Its Application for Smart Rivers: A Case Study of the Yangtze River. ISPRS International Journal of Geo-Information 2019, 8, 114 .

AMA Style

Zeqiang Chen, Nengcheng Chen. A Real-Time and Open Geographic Information System and Its Application for Smart Rivers: A Case Study of the Yangtze River. ISPRS International Journal of Geo-Information. 2019; 8 (3):114.

Chicago/Turabian Style

Zeqiang Chen; Nengcheng Chen. 2019. "A Real-Time and Open Geographic Information System and Its Application for Smart Rivers: A Case Study of the Yangtze River." ISPRS International Journal of Geo-Information 8, no. 3: 114.

Article
Published: 03 January 2019 in Climate Dynamics
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Seasonal precipitation forecasts at regional or local areas can help guide agricultural practice and urban water resource management. The North American multi-model ensemble (NMME) is a seasonal forecasting system providing precipitation forecasts globally. Bias correction and downscaling of the NMME is a critical step before applied at local scales. Here, the machine learning methods coupling with wavelet are used to correct the precipitation forecasts in NMME for 518 meteorological stations in China for eight models at 0.5–8.5 months leads. Compared with the traditional quantile mapping (QM) approach, the wavelet support vector machine (WSVM) and wavelet random forest (WRF) methods exhibit obvious advantage in downscaling, with an overall average improvement of Pearson’s correlation coefficient increasing by 0.05–0.3 and root mean square error (RMSE) reducing by 18–40 mm (21–33%) for individual models. Both the spatial and seasonal patterns of downscaled results demonstrate the superiority of wavelet machine learning methods over QM. A spatial analysis indicates that the corrected NMME precipitation forecasts show the best skill in South China, with an average RMSE of about 30 mm, while the worst skill in Central and Southwest China with a RMSE of 80 mm. In spite of the correction, the uncertainties of seasonal precipitation forecasts in summer and extreme wet cases are still large. However, the WSVM and WRF methods may serve as an effective tool in the bias correction of NMME precipitation forecasts.

ACS Style

Lei Xu; Nengcheng Chen; Xiang Zhang; Zeqiang Chen; Chuli Hu; Chao Wang. Improving the North American multi-model ensemble (NMME) precipitation forecasts at local areas using wavelet and machine learning. Climate Dynamics 2019, 53, 601 -615.

AMA Style

Lei Xu, Nengcheng Chen, Xiang Zhang, Zeqiang Chen, Chuli Hu, Chao Wang. Improving the North American multi-model ensemble (NMME) precipitation forecasts at local areas using wavelet and machine learning. Climate Dynamics. 2019; 53 (1-2):601-615.

Chicago/Turabian Style

Lei Xu; Nengcheng Chen; Xiang Zhang; Zeqiang Chen; Chuli Hu; Chao Wang. 2019. "Improving the North American multi-model ensemble (NMME) precipitation forecasts at local areas using wavelet and machine learning." Climate Dynamics 53, no. 1-2: 601-615.

Preprint content
Published: 02 January 2019
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Accurate and consistent satellite-based precipitation estimates blended with rain gauge data are important for regional precipitation monitoring and hydrological applications, especially in regions with limited rain gauges. However, existing fusion precipitation estimates often have large uncertainties over mountainous areas with complex topography and sparse rain gauges, and the existing data blending algorithms are very bad at removing the day-by-day random errors. Therefore, the development of effective methods for high-accuracy precipitation estimates over complex terrain and on a daily scale is of vital importance for mountainous hydrological applications. This study aims to offer a novel approach for blending daily precipitation gauge data, gridded precipitation data and the Climate Hazards Group Infrared Precipitation (CHIRP, daily, 0.05°) satellite-derived precipitation estimates over the Jinsha River Basin for the period of June–July–August in 2016. This method is named the Wuhan University Satellite and Gauge precipitation Collaborated Correction (WHU-SGCC). The results show that the WHU-SGCC method is effective in precipitation bias adjustments from point to surface, which is evaluated by categorical indices. Moreover, the accuracy of the spatial distribution of the precipitation estimates derived from the WHU-SGCC method is related to the complexity of the topography. The validation also verifies that the proposed approach is effective in the detection of precipitation events that are less than 20 mm. This study indicates that the WHU-SGCC approach is a promising tool to monitor monsoon precipitation over Jinsha River Basin, the complicated mountainous terrain with sparse rain gauge data, considering the spatial correlation and the historical precipitation characteristics. The daily precipitation estimations at 0.05° resolution over Jinsha River Basin in summer 2016, derived from WHU-SGCC are available at the PANGAEA Data Publisher for Earth & Environmental Science portal (https://doi.pangaea.de/10.1594/PANGAEA.896615).

ACS Style

Gaoyun Shen; Nengcheng Chen; Wei Wang; Zeqiang Chen. WHU-SGCC: A novel approach for blending daily satellite (CHIRP) and precipitation observations over Jinsha River Basin. 2019, 2019, 1 -23.

AMA Style

Gaoyun Shen, Nengcheng Chen, Wei Wang, Zeqiang Chen. WHU-SGCC: A novel approach for blending daily satellite (CHIRP) and precipitation observations over Jinsha River Basin. . 2019; 2019 ():1-23.

Chicago/Turabian Style

Gaoyun Shen; Nengcheng Chen; Wei Wang; Zeqiang Chen. 2019. "WHU-SGCC: A novel approach for blending daily satellite (CHIRP) and precipitation observations over Jinsha River Basin." 2019, no. : 1-23.

Journal article
Published: 01 July 2018 in Computers & Geosciences
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ACS Style

Zeqiang Chen; Nengcheng Chen; Wenying Du; Jianya Gong. An active monitoring method for flood events. Computers & Geosciences 2018, 116, 42 -52.

AMA Style

Zeqiang Chen, Nengcheng Chen, Wenying Du, Jianya Gong. An active monitoring method for flood events. Computers & Geosciences. 2018; 116 ():42-52.

Chicago/Turabian Style

Zeqiang Chen; Nengcheng Chen; Wenying Du; Jianya Gong. 2018. "An active monitoring method for flood events." Computers & Geosciences 116, no. : 42-52.

Conference paper
Published: 01 June 2018 in 2018 26th International Conference on Geoinformatics
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Natural disaster events worldwide occur frequently. They are heterogeneous, dynamic, and may occur in sequence. Additionally, some of these natural disasters may be a complex of multiple simplex events. Duo to the lack of any uniform event modeling criteria and platform, it's difficult to collaborate and reuse the disaster events information. In view of the cycle of natural disasters, this paper analyzed the existing methods of describing disaster events and presented a framework for disaster events modeling and management based on OGC EML standard. A case study of the Liangzi Lake flood event occurred in summer 2010 was used to illustrate the feasibility of the proposed framework in modeling natural disaster events. Furthermore, it can also provide important support in other environmental applications.

ACS Style

Chao Wang; Wenyin Du; Zeqiang Chen; Nengcheng Chen; Wei Wang. An Event Modeling Software for Natural Disasters: Design and Implementation. 2018 26th International Conference on Geoinformatics 2018, 1 -4.

AMA Style

Chao Wang, Wenyin Du, Zeqiang Chen, Nengcheng Chen, Wei Wang. An Event Modeling Software for Natural Disasters: Design and Implementation. 2018 26th International Conference on Geoinformatics. 2018; ():1-4.

Chicago/Turabian Style

Chao Wang; Wenyin Du; Zeqiang Chen; Nengcheng Chen; Wei Wang. 2018. "An Event Modeling Software for Natural Disasters: Design and Implementation." 2018 26th International Conference on Geoinformatics , no. : 1-4.

Conference paper
Published: 01 June 2018 in 2018 26th International Conference on Geoinformatics
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Due to the lack of a unified and comprehensive description of the dynamic disaster process monitoring, it is a great challenge to quickly and effectively share and discover more linked information resources in sensor web for the improvement of decision-making efficiency. A sensor web resource ontology for the dynamic disaster process monitoring was proposed in this paper, which can focus on the characteristics of the observation resources for diverse applications during different monitoring processes. To verify the feasibility of the method, case studies were conducted, including the semantic modeling of the in-situ monitoring and the remote sensing monitoring for flood disaster processes. The results demonstrated the effective sharing of various observation resources with different processes for environmental monitoring.

ACS Style

Nengcheng Chen; Yingbing Liu; Chao Wang; Chang Xiong; Zeqiang Chen; Changjiang Xiao. SWRO-DDPM: A Sensor Web Resource Ontology for the Dynamic Disaster Process Monitoring. 2018 26th International Conference on Geoinformatics 2018, 1 -4.

AMA Style

Nengcheng Chen, Yingbing Liu, Chao Wang, Chang Xiong, Zeqiang Chen, Changjiang Xiao. SWRO-DDPM: A Sensor Web Resource Ontology for the Dynamic Disaster Process Monitoring. 2018 26th International Conference on Geoinformatics. 2018; ():1-4.

Chicago/Turabian Style

Nengcheng Chen; Yingbing Liu; Chao Wang; Chang Xiong; Zeqiang Chen; Changjiang Xiao. 2018. "SWRO-DDPM: A Sensor Web Resource Ontology for the Dynamic Disaster Process Monitoring." 2018 26th International Conference on Geoinformatics , no. : 1-4.