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Prof. Chunlin Huang
NIEER, CAS

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0 Data Assimilation
0 Remote Sensing
0 Snow
0 Soil moisture
0 evaportranspiration

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Journal article
Published: 29 July 2021 in Remote Sensing
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Accurate estimation of crop area is essential to adjusting the regional crop planting structure and the rational planning of water resources. However, it is quite challenging to map crops accurately by high-resolution remote sensing images because of the ecological gradient and ecological convergence between crops and non-crops. The purpose of this study is to explore the combining application of high-resolution multi-temporal Sentinel-1 (S1) radar backscatter and Sentinel-2 (S2) optical reflectance images for maize mapping in highly complex and heterogeneous landscapes in the middle reaches of Heihe River, northwest China. We proposed a new two-step method of vegetation extraction and followed by maize extraction, that is, extract the vegetation-covered areas first to reduce the inter-class variance by using a Random Forest (RF) classifier based on S2 data, and then extract the maize distribution in the vegetation area by using another RF classifier based on S1 and/or S2 data. The results demonstrate that the vegetation extraction classifier successfully identified vegetation-covered regions with an overall accuracy above 96% in the study area, and the accuracy of the maize extraction classifier constructed by the combined multi-temporal S1 and S2 images is significantly improved compared with that S1 (alone) or S2 (alone), with an overall accuracy of 87.63%, F1_Score of 0.86, and Kappa coefficient of 0.75. In addition, with the introduction of multi-temporal S1 and/or S2 images in crop growing season, the constructed RF model is more beneficial to maize mapping.

ACS Style

Yansi Chen; Jinliang Hou; Chunlin Huang; Ying Zhang; Xianghua Li. Mapping Maize Area in Heterogeneous Agricultural Landscape with Multi-Temporal Sentinel-1 and Sentinel-2 Images Based on Random Forest. Remote Sensing 2021, 13, 2988 .

AMA Style

Yansi Chen, Jinliang Hou, Chunlin Huang, Ying Zhang, Xianghua Li. Mapping Maize Area in Heterogeneous Agricultural Landscape with Multi-Temporal Sentinel-1 and Sentinel-2 Images Based on Random Forest. Remote Sensing. 2021; 13 (15):2988.

Chicago/Turabian Style

Yansi Chen; Jinliang Hou; Chunlin Huang; Ying Zhang; Xianghua Li. 2021. "Mapping Maize Area in Heterogeneous Agricultural Landscape with Multi-Temporal Sentinel-1 and Sentinel-2 Images Based on Random Forest." Remote Sensing 13, no. 15: 2988.

Research article
Published: 03 July 2021 in Hydrological Processes
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A snow depletion curve (SDC), the relationship between snow mass (e.g., snow depth (SD)) and fractional snow cover area (SCF), is essential to parameterize the effect of snowpack within a physically based snow model. Existing SDCs are constructed using traditional statistic methods may not be applicable in complex mountainous areas. In this study, we developed an information fusion framework to define the relationship between SCF and SD as well as 12 auxiliary factors by using a traditional statistical method and four prevailing machine learning (ML) algorithms, which have comprehensively considered the variable conditions that cause spatiotemporal heterogeneity of snow cover. We also performed a single-dimensional sensitivity analysis to investigate the physical rationality of the newly developed SDCs. The Northern Xinjiang, Northwest China, is selected as the study area, and the data from 46 meteorological stations covering five snow seasons from 2010 to 2015 are used. The results illustrated that ML techniques can be used to establish high-accuracy and robust SDCs for complex mountainous areas. Compared with SDCs constructed by traditional statistical, the performance of the four ML-based SDCs is significantly improved, the RMSE values can be reduced by 50%, R2 above 0.75, and an average relative variance close to 0. ML-based SDCs predicted SCF values showed a range of sensitivities to different input variables (e.g., Land surface temperature, aspect, longwave radiation, and land cover type), in addition to SD, that were physically representative of effects that snow cover is sensitive to. Moreover, the complexity of SDCs can be reduced by removing insensitive input variables.

ACS Style

Jinliang Hou; Chunlin Huang; Weijing Chen; Ying Zhang. Developing machine learning‐based snow depletion curves and analysing their sensitivity over complex mountainous areas. Hydrological Processes 2021, 35, e14303 .

AMA Style

Jinliang Hou, Chunlin Huang, Weijing Chen, Ying Zhang. Developing machine learning‐based snow depletion curves and analysing their sensitivity over complex mountainous areas. Hydrological Processes. 2021; 35 (8):e14303.

Chicago/Turabian Style

Jinliang Hou; Chunlin Huang; Weijing Chen; Ying Zhang. 2021. "Developing machine learning‐based snow depletion curves and analysing their sensitivity over complex mountainous areas." Hydrological Processes 35, no. 8: e14303.

Journal article
Published: 23 June 2021 in Water Resources Research
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In this study, an innovative MODIS fractional snow cover (SCF) data assimilation (DA) prototype framework that invokes machine learning (ML) techniques and Common land model (CoLM) is proposed to improve the estimation of the snow depth (SD) and the SCF. To validate our new framework, we analyzed two snow seasons from 2013 to 2015 at 46 stations in Northern Xinjiang in China. We developed 12 SCF DA schemes that represent different DA methods (direct insertion (DI) and Ensemble Kalman Filter (EnKF)), observational data (original data and gap-filled MODIS SCF data), and observation operators (five new snow depletion curves (SDCs) defined using traditional multivariate nonlinear regression and four ML methods). While improving the frequency of the SCF observations in the DI-based DA scheme only resulted in a marginal improvement in the snow estimates, by adding new SDCs fitted by ML techniques (e.g., deep belief network), and the gap-filled MODIS SCF data to the EnKF-based DA scheme, we were able to reduce model structural uncertainties of CoLM and achieve marked improvement in the accuracy of the snow estimates (RMSE = 5.92 cm, mean bias error = −1.94 cm, and average degree of improvement = 32.18% for SD estimates and RMSE = 15. 79%, mean bias error = −1.21%, and average degree of improvement = 47.95% for SCF estimates). Our results demonstrate the feasibility of improving snow estimates by combining the ML techniques with physically based snowpack model in a SCF DA framework.

ACS Style

Jinliang Hou; Chunlin Huang; Weijing Chen; Ying Zhang. Improving Snow Estimates Through Assimilation of MODIS Fractional Snow Cover Data Using Machine Learning Algorithms and the Common Land Model. Water Resources Research 2021, 57, 1 .

AMA Style

Jinliang Hou, Chunlin Huang, Weijing Chen, Ying Zhang. Improving Snow Estimates Through Assimilation of MODIS Fractional Snow Cover Data Using Machine Learning Algorithms and the Common Land Model. Water Resources Research. 2021; 57 (7):1.

Chicago/Turabian Style

Jinliang Hou; Chunlin Huang; Weijing Chen; Ying Zhang. 2021. "Improving Snow Estimates Through Assimilation of MODIS Fractional Snow Cover Data Using Machine Learning Algorithms and the Common Land Model." Water Resources Research 57, no. 7: 1.

Journal article
Published: 27 April 2021 in Remote Sensing
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The lake ice phenology variations are vital for the land–surface–water cycle. Qinghai Lake is experiencing amplified warming under climate change. Based on the MODIS imagery, the spatio-temporal dynamics of the ice phenology of Qinghai Lake were analyzed using machine learning during the 2000/2001 to 2019/2020 ice season, and cloud gap-filling procedures were applied to reconstruct the result. The results showed that the overall accuracy of the water–ice classification by random forest and cloud gap-filling procedures was 98.36% and 92.56%, respectively. The annual spatial distribution of the freeze-up and break-up dates ranged primarily from DOY 330 to 397 and from DOY 70 to 116. Meanwhile, the decrease rates of freeze-up duration (DFU), full ice cover duration (DFI), and ice cover duration (DI) were 0.37, 0.34, and 0.13 days/yr., respectively, and the duration was shortened by 7.4, 6.8, and 2.6 days over the past 20 years. The increased rate of break-up duration (DBU) was 0.58 days/yr. and the duration was lengthened by 11.6 days. Furthermore, the increase in temperature resulted in an increase in precipitation after two years; the increase in precipitation resulted in the increase in DBU and decrease in DFU in corresponding years, and decreased DI and DFI after one year.

ACS Style

Weixiao Han; Chunlin Huang; Juan Gu; Jinliang Hou; Ying Zhang. Spatial-Temporal Distribution of the Freeze–Thaw Cycle of the Largest Lake (Qinghai Lake) in China Based on Machine Learning and MODIS from 2000 to 2020. Remote Sensing 2021, 13, 1695 .

AMA Style

Weixiao Han, Chunlin Huang, Juan Gu, Jinliang Hou, Ying Zhang. Spatial-Temporal Distribution of the Freeze–Thaw Cycle of the Largest Lake (Qinghai Lake) in China Based on Machine Learning and MODIS from 2000 to 2020. Remote Sensing. 2021; 13 (9):1695.

Chicago/Turabian Style

Weixiao Han; Chunlin Huang; Juan Gu; Jinliang Hou; Ying Zhang. 2021. "Spatial-Temporal Distribution of the Freeze–Thaw Cycle of the Largest Lake (Qinghai Lake) in China Based on Machine Learning and MODIS from 2000 to 2020." Remote Sensing 13, no. 9: 1695.

Journal article
Published: 15 December 2020 in Remote Sensing
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Lake phenology is essential for understanding the lake freeze-thaw cycle effects on terrestrial hydrological processes. The Qinghai-Tibetan Plateau (QTP) has the most extensive ice reserve outside of the Arctic and Antarctic poles and is a sensitive indicator of global climate changes. Qinghai Lake, the largest lake in the QTP, plays a critical role in climate change. The freeze-thaw cycles of lakes were studied using daily Moderate Resolution Imaging Spectroradiometer (MODIS) data ranging from 2000–2018 in the Google Earth Engine (GEE) platform. Surface water/ice area, coverage, critical dates, surface water, and ice cover duration were extracted. Random forest (RF) was applied with a classifier accuracy of 0.9965 and a validation accuracy of 0.8072. Compared with six common water indexes (tasseled cap wetness (TCW), normalized difference water index (NDWI), modified normalized difference water index (MNDWI), automated water extraction index (AWEI), water index 2015 (WI2015) and multiband water index (MBWI)) and ice threshold value methods, the critical freeze-up start (FUS), freeze-up end (FUE), break-up start (BUS), and break-up end (BUE) dates were extracted by RF and validated by visual interpretation. The results showed an R2 of 0.99, RMSE of 3.81 days, FUS and BUS overestimations of 2.50 days, and FUE and BUE underestimations of 0.85 days. RF performed well for lake freeze-thaw cycles. From 2000 to 2018, the FUS and FUE dates were delayed by 11.21 and 8.21 days, respectively, and the BUS and BUE dates were 8.59 and 1.26 days early, respectively. Two novel key indicators, namely date of the first negative land surface temperature (DFNLST) and date of the first positive land surface temperature (DFPLST), were proposed to comprehensively delineate lake phenology: DFNLST was approximately 37 days before FUS, and DFPLST was approximately 20 days before BUS, revealing that the first negative and first positive land surface temperatures occur increasingly earlier.

ACS Style

Weixiao Han; Chunlin Huang; Hongtao Duan; Juan Gu; Jinliang Hou. Lake Phenology of Freeze-Thaw Cycles Using Random Forest: A Case Study of Qinghai Lake. Remote Sensing 2020, 12, 4098 .

AMA Style

Weixiao Han, Chunlin Huang, Hongtao Duan, Juan Gu, Jinliang Hou. Lake Phenology of Freeze-Thaw Cycles Using Random Forest: A Case Study of Qinghai Lake. Remote Sensing. 2020; 12 (24):4098.

Chicago/Turabian Style

Weixiao Han; Chunlin Huang; Hongtao Duan; Juan Gu; Jinliang Hou. 2020. "Lake Phenology of Freeze-Thaw Cycles Using Random Forest: A Case Study of Qinghai Lake." Remote Sensing 12, no. 24: 4098.

Journal article
Published: 06 November 2020 in Remote Sensing
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Understanding the spatial distribution of populations at a finer spatial scale has important value for many applications, such as disaster risk rescue operations, business decision-making, and regional planning. In this study, a random forest (RF)-based population density mapping method was proposed in order to generate high-precision population density data with a 100 m × 100 m grid in mainland China in 2015 (hereafter referred to as ‘Popi’). Besides the commonly used elevation, slope, Normalized Vegetation Index (NDVI), land use/land cover, roads, and National Polar Orbiting Partnership/Visible Infrared Imaging Radiometer Suite (NPP/VIIRS), 16,101,762 records of points of interest (POIs) and 2867 county-level censuses were used in order to develop the model. Furthermore, 28,505 township-level censuses (74% of the total number of townships) were collected in order to evaluate the accuracy of the Popi product. The results showed that the utilization of multi-source data (especially the combination of POIs and NPP/VIIRS data) can effectively improve the accuracy of population mapping at a finer scale. The feature importances of the POIs and NPP/VIIRS are 0.49 and 0.14, respectively, which are higher values than those obtained for other natural factors. Compared with the Worldpop population dataset, the Popi data exhibited a higher accuracy. The number of accurately-estimated townships was 19,300 (67.7%) in the Popi product and 16,237 (56.9%) in the Worldpop product. The Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) were 14839 and 7218, respectively, for Popi, and 18014 and 8572, respectively, for Worldpop. The research method in this paper could provide a reference for the spatialization of other socioeconomic data (such as GDP).

ACS Style

Yunchen Wang; Chunlin Huang; Minyan Zhao; Jinliang Hou; Ying Zhang; Juan Gu. Mapping the Population Density in Mainland China using NPP/VIIRS and Points-Of-Interest Data Based on a Random Forests Model. Remote Sensing 2020, 12, 3645 .

AMA Style

Yunchen Wang, Chunlin Huang, Minyan Zhao, Jinliang Hou, Ying Zhang, Juan Gu. Mapping the Population Density in Mainland China using NPP/VIIRS and Points-Of-Interest Data Based on a Random Forests Model. Remote Sensing. 2020; 12 (21):3645.

Chicago/Turabian Style

Yunchen Wang; Chunlin Huang; Minyan Zhao; Jinliang Hou; Ying Zhang; Juan Gu. 2020. "Mapping the Population Density in Mainland China using NPP/VIIRS and Points-Of-Interest Data Based on a Random Forests Model." Remote Sensing 12, no. 21: 3645.

Journal article
Published: 03 October 2020 in Remote Sensing
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Daily evapotranspiration (ET) and its components of evaporation (E) and transpiration (T) at field scale are often required for improving agricultural water management and maintaining ecosystem health, especially in semiarid and arid regions. In this study, multi-year daily ET, E, and T at a spatial resolution of 100 m in the middle reaches of Heihe River Basin were computed based on an ET partitioning method developed by combing remote sensing-based ET model and multi-satellite data fusion methodology. Evaluations using flux tower measurements over irrigated cropland and natural desert sites indicate that this method can provide reliable estimates of surface flux partitioning and daily ET. Modeled daily ET yielded root mean square error (RMSE) values of 0.85 mm for cropland site and 0.84 mm for desert site, respectively. The E and T partitioning capabilities of this proposed method was further assessed by using ratios E/ET and T/ET derived from isotopic technology at the irrigated cropland site. Results show that apart from early in the growing season when the actual E was reduced by plastic film mulching, the modeled E/ET and T/ET agree well with observations in terms of both magnitude and temporal dynamics. The multi-year seasonal patterns of modeled ET, E, and T at field scale from this ET partitioning method shows reasonable seasonal variation and spatial variability, which can be used for monitoring plant water consumption in both agricultural and natural ecosystems.

ACS Style

Yan Li; Chunlin Huang; William Kustas; Hector Nieto; Liang Sun; Jinliang Hou. Evapotranspiration Partitioning at Field Scales Using TSEB and Multi-Satellite Data Fusion in The Middle Reaches of Heihe River Basin, Northwest China. Remote Sensing 2020, 12, 3223 .

AMA Style

Yan Li, Chunlin Huang, William Kustas, Hector Nieto, Liang Sun, Jinliang Hou. Evapotranspiration Partitioning at Field Scales Using TSEB and Multi-Satellite Data Fusion in The Middle Reaches of Heihe River Basin, Northwest China. Remote Sensing. 2020; 12 (19):3223.

Chicago/Turabian Style

Yan Li; Chunlin Huang; William Kustas; Hector Nieto; Liang Sun; Jinliang Hou. 2020. "Evapotranspiration Partitioning at Field Scales Using TSEB and Multi-Satellite Data Fusion in The Middle Reaches of Heihe River Basin, Northwest China." Remote Sensing 12, no. 19: 3223.

Journal article
Published: 24 June 2020 in Remote Sensing of Environment
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The Sustainable Development Goal (SDG) 6.3.2 of the United Nations (UN) focuses on ambient water quality, while water clarity simplistically and visually reflect water quality and can potentially support SDG 6.3.2 reporting. In this study, based on extensive field data and Sentinel-3 Ocean and Land Color Instrument (OLCI) imagery, a random forest regression (RFR) Secchi depth (Zsd) model suitable for turbid and eutrophic waters was established. With this model, the Zsd of 86 large (> 30 km2) lakes in Eastern China was obtained from May 2016 to April 2018. Additionally, the potential for applying OLCI-derived Zsd data in the SDG 6.3.2 evaluation was assessed. Of six common atmospheric correction (AC) processors (i.e., BAC, C2RCC, POLYMER, BP, MUMM, and 6SV), 6SV often exhibited the best performance except for at 754 nm (root mean square error (RMSE) ≤ 0.0094 sr−1, mean absolute percentage error (MAPE) ≤ 36.27%, and mean normalized bias (MNB) ≤ 15.89%). The RFR model had higher accuracy (R2 = 0.70, RMSE = 0.13 m, MAPE = 33.43%, and MNB = 14.55%) and was more suitable for eutrophic and turbid inland lakes across Eastern China than the other available Zsd algorithms. The average OLCI-derived Zsd of the lakes in Eastern China was 0.44 ± 0.13 m, suggesting that these lakes are extremely turbid. The average Zsd of lakes in the Eastern Plain Lake (EPL) zone (0.45 ± 0.12 m) was higher than that of the lakes in the Northeastern Plain and Mountain Lake (NPML) zone (0.40 ± 0.17 m). The majority of lakes showed higher Zsd values in summer (rainy season) than in fall and winter (dry season). A simple SDG 6.3.2 evaluation scheme was developed based on the Zsd product, and only 54.65% of the lakes (N = 47) reached the “good” level during the monitoring period as a result of the eutrophication of the lakes in Eastern China. This study provides water clarity information for large lakes in Eastern China and facilitates the understanding of ambient water quality under the 2030 UN SDG framework as well as data and technical support for future SDG 6.3.2 evaluations.

ACS Style

Ming Shen; Hongtao Duan; Zhigang Cao; Kun Xue; Tianci Qi; Jinge Ma; Dong Liu; Kaishan Song; Chunlin Huang; Xiaoyu Song. Sentinel-3 OLCI observations of water clarity in large lakes in eastern China: Implications for SDG 6.3.2 evaluation. Remote Sensing of Environment 2020, 247, 111950 .

AMA Style

Ming Shen, Hongtao Duan, Zhigang Cao, Kun Xue, Tianci Qi, Jinge Ma, Dong Liu, Kaishan Song, Chunlin Huang, Xiaoyu Song. Sentinel-3 OLCI observations of water clarity in large lakes in eastern China: Implications for SDG 6.3.2 evaluation. Remote Sensing of Environment. 2020; 247 ():111950.

Chicago/Turabian Style

Ming Shen; Hongtao Duan; Zhigang Cao; Kun Xue; Tianci Qi; Jinge Ma; Dong Liu; Kaishan Song; Chunlin Huang; Xiaoyu Song. 2020. "Sentinel-3 OLCI observations of water clarity in large lakes in eastern China: Implications for SDG 6.3.2 evaluation." Remote Sensing of Environment 247, no. : 111950.

Journal article
Published: 05 May 2020 in Sustainability
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Under economic fluctuations, the sustainable development of enterprises is crucial. Currently, there are few studies on the interaction between economic policy uncertainty (EPU) and the sustainable development behavior of enterprises. Based on a panel vector autoregressive (PVAR) model, this paper explores the static and dynamic interactions among EPU, enterprise investment, and enterprise profitability and then analyzes regional heterogeneity in these factors. It finds that EPU has an inhibitory effect on the investment and profitability of enterprises, while the investment and profitability of enterprises also have an inhibitory effect on EPU. In addition, there are contribution differences and regional differences in the degrees of influence of the three factors. In the long run, EPU and the inhibition of enterprise investment and profitability are strongest in China’s central region. The results show that the stronger the certainty of economic policy, the more conducive this policy is to promoting enterprise investment behavior and improving enterprise profitability. Therefore, to ensure normal economic development, the government should limit changes in economic policy as much as possible; doing so is critical for promoting investment behavior and improving the profitability of enterprises.

ACS Style

Aijun Guo; Haiqi Wei; Fanglei Zhong; Shuangshuang Liu; Chunlin Huang. Enterprise Sustainability: Economic Policy Uncertainty, Enterprise Investment, and Profitability. Sustainability 2020, 12, 3735 .

AMA Style

Aijun Guo, Haiqi Wei, Fanglei Zhong, Shuangshuang Liu, Chunlin Huang. Enterprise Sustainability: Economic Policy Uncertainty, Enterprise Investment, and Profitability. Sustainability. 2020; 12 (9):3735.

Chicago/Turabian Style

Aijun Guo; Haiqi Wei; Fanglei Zhong; Shuangshuang Liu; Chunlin Huang. 2020. "Enterprise Sustainability: Economic Policy Uncertainty, Enterprise Investment, and Profitability." Sustainability 12, no. 9: 3735.

Preprint content
Published: 23 March 2020
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In semiarid and arid regions, irrigated agriculture consumes most of water resources via evapotranspiration (ET) that mainly consists of evaporation (E) from bare soil and transpiration (T) from plant tissue. Generally, T is regarded as beneficial water use that contribute to plant production but E is considered as water waste. Therefore, daily ET and ET components E and T at filed scale are often required for improving water resource management strategy in semiarid and arid regions. Recently, time-continuous daily ET at filed scale have been achieved based on remote sensing-based ET model and multi-satellite data fusion, but few study focus on estimating of daily field-scale ET component of E and T. In this study, a daily filed-scale ET partitioning method based on the two source energy balance (TSEB) model and the spatial and temporal adaptive reflectance fusion model (STARFM) was applied and verified in a typical arid area dominated by irrigated cropland and natural desert. The comparisons of instantaneous land surface fluxes and daily ET modeled from proposed method and that derived from eddy covariance (EC) systems and automated weather stations (AWS) set up in irrigated cropland and desert indicate that reasonable surface fluxes partitioning and daily ET can be estimated by using this method. The root mean square error (RMSE) for cropland and desert are 0.87 mm and 0.84 mm, respectively. Evaluations of E and T partitioning capabilities of this proposed method based on E/ET and T/ET derived from isotopic technology at the irrigated cropland site show that the modeled E/ET and T/ET agree well with observations in terms of both magnitude and dynamics. Finally, the multi-year spatiotemporal patterns of modeled ET, E and T at filed scale with reasonable seasonal variation and spatial diversity were produced using the ET partitioning method to provide reasonable information for monitoring water use in study area.

ACS Style

Yan Li; Chunlin Huang. Estimating daily evaporation and transpiration at field scale (100 m) based on TSEB and data fusion using MODIS and Landsat data in irrigated agriculture area. 2020, 1 .

AMA Style

Yan Li, Chunlin Huang. Estimating daily evaporation and transpiration at field scale (100 m) based on TSEB and data fusion using MODIS and Landsat data in irrigated agriculture area. . 2020; ():1.

Chicago/Turabian Style

Yan Li; Chunlin Huang. 2020. "Estimating daily evaporation and transpiration at field scale (100 m) based on TSEB and data fusion using MODIS and Landsat data in irrigated agriculture area." , no. : 1.

Journal article
Published: 16 March 2020 in Sustainability
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For more efficient development planning, food-energy-water (FEW) nexus indicators should be provided with higher spatial and temporal resolutions. This paper takes Zhangye, a typical oasis city in Northwest China’s arid region, as an example, and uses the unweighted, geometric mean method to calculate a standardized, quantitative, and transparent estimation of the FEW nexus for each county. The role of influencing factors is also analyzed. The results showed that (1) the coordination of the FEW nexus in each county gradually increased from 2005 to 2015. Spatially, the distribution of the FEW nexus showed a tendency to be higher in the southwestern region and lower in the northeastern region. (2) Food security and water security were weaker than energy security. Specifically, there were more limitations to food accessibility, water availability, and water accessibility than for other indexes. (3) The FEW indexes are positively associated with per capita GDP (Gross Domestic Product) and negatively correlated with the average evaporation and altitude of each county (district). Decision makers should concentrate on combining industrial advantages, developing water-efficient ecological agriculture, and improving production quality to increase market competitiveness and should actively explore the international market.

ACS Style

Yaya Feng; Fanglei Zhong; Chunlin Huang; Juan Gu; Yingchun Ge; Xiaoyu Song. Spatiotemporal Distribution and the Driving Force of the Food-Energy-Water Nexus Index in Zhangye, Northwest China. Sustainability 2020, 12, 2309 .

AMA Style

Yaya Feng, Fanglei Zhong, Chunlin Huang, Juan Gu, Yingchun Ge, Xiaoyu Song. Spatiotemporal Distribution and the Driving Force of the Food-Energy-Water Nexus Index in Zhangye, Northwest China. Sustainability. 2020; 12 (6):2309.

Chicago/Turabian Style

Yaya Feng; Fanglei Zhong; Chunlin Huang; Juan Gu; Yingchun Ge; Xiaoyu Song. 2020. "Spatiotemporal Distribution and the Driving Force of the Food-Energy-Water Nexus Index in Zhangye, Northwest China." Sustainability 12, no. 6: 2309.

Journal article
Published: 21 February 2020 in Earth and Space Science
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An effective assessment of future climate change, especially future precipitation forecasting, is an important basis for the rational development of adaptive strategies for Northwest China, where the ecological environment is fragile and encompasses arid and semiarid regions. In this work, the performance of a regional climate model is assessed; then, climate changes in the near future (2018‐2037), middle future (2050‐2069) and distant future (2080‐2099) are analyzed under representative concentration pathways (RCPs) RCP2.6, RCP4.5, and RCP8.5. The following conclusions are drawn: (1) Compared to the Met Office Hadley Centre Earth System (HadGEM2‐ES) global climate model, the latest regional climate model, RegCM4.6, with a community land model (CLM) land surface process scheme and Tiedtke cumulus convective parameterization, can create a good simulation of the present‐day mean climatology over Northwest China, including temperature, precipitation, and climate extremes, and can also provide finer‐scale climate information in complex terrain and better correct the cold bias than HadGEM2‐ES. At the same time, RegCM4 inherited the bias from HadGEM2‐ES, for example, both the RegCM4 and the HadGEM2‐ES overestimated precipitation in DJF in the southeast of the study area. (2) The future near surface air temperature will experience continuous warming over Northwest China under the RCP8.5 scenario, and the warming will become more significant and exceed 6°C by the end of the 21st century. In RegCM4, future precipitation will continue to increase and will increase by 50 mm by the end of the 21st century relative to historical data. The extreme climate index summer days (SU) will continue to increase, indicating that high temperatures will be more frequent in Northwest China. In contrast, the consecutive dry days (CDD) will decrease, likely because of the increase in precipitation.

ACS Style

X. D. Pan; L. Zhang; C. L. Huang. Future Climate Projection in Northwest China With RegCM4.6. Earth and Space Science 2020, 7, 1 .

AMA Style

X. D. Pan, L. Zhang, C. L. Huang. Future Climate Projection in Northwest China With RegCM4.6. Earth and Space Science. 2020; 7 (2):1.

Chicago/Turabian Style

X. D. Pan; L. Zhang; C. L. Huang. 2020. "Future Climate Projection in Northwest China With RegCM4.6." Earth and Space Science 7, no. 2: 1.

Journal article
Published: 16 February 2020 in Journal of Geophysical Research: Atmospheres
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This study assessed the sensitivity and uncertainty interval of the Noah land surface model with multi‐parameterization (Noah‐MP) using observed meteorological data from eight sites with different snow climates. A total number of 20736 Noah‐MP physics‐ensemble simulations were conducted for each site by combining different parameterization schemes of physical processes. A natural selection approach and Tukey's test were used to analyze the sensitivity of simulated snow depth to parameterization schemes. The sensitivity of parameterizations at each site was discussed, and the uncertainty interval of sensitive parameterizations was further explored. The results of sensitivity analyses showed the following. The parameterizations for the first‐layer snow or soil temperature time scheme, the lower boundary condition of soil temperature, and the partitioning of precipitation into rainfall and snowfall showed sensitivity at all sites. Snow surface albedo parameterizations showed sensitivity at all sites except for two sites in China. Parameterizations for vegetation canopy and canopy stomatal resistance showed sensitivity at some sites. Further comparative results indicated that uncertainties in multi‐parameterization ensemble simulations were mainly due to sensitive parameterizations under the condition of disregarding the uncertainties from forcing and parameters. After removing the sensitive parameterization schemes that were notably poor‐performing, the uncertainty interval in the ensemble simulations decreased significantly. Finally, we concluded that an optimal combination group of well‐performed sensitive parameterization schemes can be configured based on the results of sensitivity analysis.

ACS Style

Yuanhong You; Chunlin Huang; Zongliang Yang; Ying Zhang; Yulong Bai; Juan Gu. Assessing Noah‐MP Parameterization Sensitivity and Uncertainty Interval Across Snow Climates. Journal of Geophysical Research: Atmospheres 2020, 125, 1 .

AMA Style

Yuanhong You, Chunlin Huang, Zongliang Yang, Ying Zhang, Yulong Bai, Juan Gu. Assessing Noah‐MP Parameterization Sensitivity and Uncertainty Interval Across Snow Climates. Journal of Geophysical Research: Atmospheres. 2020; 125 (4):1.

Chicago/Turabian Style

Yuanhong You; Chunlin Huang; Zongliang Yang; Ying Zhang; Yulong Bai; Juan Gu. 2020. "Assessing Noah‐MP Parameterization Sensitivity and Uncertainty Interval Across Snow Climates." Journal of Geophysical Research: Atmospheres 125, no. 4: 1.

Journal article
Published: 23 January 2020 in Environmental Modelling & Software
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Common software for land data assimilation is urgently needed to implement a wide variety of assimilation applications; however, a fast, easy-to-use, and multidisciplinary application-oriented assimilation platform has not been achieved. Therefore, we developed Common software for Nonlinear and non-Gaussian Land Data Assimilation (ComDA). ComDA integrates multiple algorithms (including diverse Kalman and particle filters), models and observation operators (e.g., common land model (CoLM), Advanced Integral Equation Model (AIEM)), and provides general interfaces for additional operators. Using mixed-language programming and parallel computing technologies (Open Multi-Processing (OpenMP), Message Passing Interface (MPI) and Compute Unified Device Architecture (CUDA)), ComDA can assimilate various land surface variables and remote sensing observations. High-performance computing and synthetic tests and real-world tests indicate that ComDA achieves the standard of common land data assimilation software with parallel computation, multiple operators, and assimilation algorithms and is compatible with many models. ComDA can be applied for multidisciplinary data assimilation.

ACS Style

Feng Liu; Liangxu Wang; Xin Li; Chunlin Huang. ComDA: A common software for nonlinear and Non-Gaussian Land Data Assimilation. Environmental Modelling & Software 2020, 127, 104638 .

AMA Style

Feng Liu, Liangxu Wang, Xin Li, Chunlin Huang. ComDA: A common software for nonlinear and Non-Gaussian Land Data Assimilation. Environmental Modelling & Software. 2020; 127 ():104638.

Chicago/Turabian Style

Feng Liu; Liangxu Wang; Xin Li; Chunlin Huang. 2020. "ComDA: A common software for nonlinear and Non-Gaussian Land Data Assimilation." Environmental Modelling & Software 127, no. : 104638.

Journal article
Published: 22 January 2020 in Remote Sensing
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Urban sustainable development has attracted widespread attention worldwide as it is closely linked with human survival. However, the growth of urban areas is frequently disproportionate in relation to population growth in developing countries; this discrepancy cannot be monitored solely using statistics. In this study, we integrated earth observation (EO) and statistical data monitoring the Sustainable Development Goals (SDG) 11.3.1: “The ratio of land consumption rate to the population growth rate (LCRPGR)”. Using the EO data (including China’s Land-Use/Cover Datasets (CLUDs) and the Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) nighttime light data) and census, we extracted the percentage of built-up area, disaggregated the population using the geographically weighted regression (GWR) model, and depicted the spatial heterogeneity and dynamic tendency of urban expansion and population growth by a 1 km × 1 km grid at city and national levels in mainland China from 1990 to 2010. Then, the built-up area and population density datasets were compared with other products and statistics using the relative error and standard deviation in our research area. Major findings are as follows: (1) more than 95% of cities experienced growth in urban built-up areas, especially in the megacities with populations of 5–10 million; (2) the number of grids with a declined proportion of the population ranged from 47% in 1990–2000 to 54% in 2000–2010; (3) China’s LCRPGR value increased from 1.69 in 1990–2000 to 1.78 in 2000–2010, and the land consumption rate was 1.8 times higher than the population growth rate from 1990 to 2010; and (4) the number of cities experiencing uncoordinated development (i.e., where urban expansion is not synchronized with population growth) increased from 93 (27%) in 1990–2000 to 186 (54%) in 2000–2010. Using EO has the potential for monitoring the official SDGs on large and fine scales; the processes provide an example of the localization of SDG 11.3.1 in China.

ACS Style

Yunchen Wang; Chunlin Huang; Yaya Feng; Minyan Zhao; Juan Gu. Using Earth Observation for Monitoring SDG 11.3.1-Ratio of Land Consumption Rate to Population Growth Rate in Mainland China. Remote Sensing 2020, 12, 357 .

AMA Style

Yunchen Wang, Chunlin Huang, Yaya Feng, Minyan Zhao, Juan Gu. Using Earth Observation for Monitoring SDG 11.3.1-Ratio of Land Consumption Rate to Population Growth Rate in Mainland China. Remote Sensing. 2020; 12 (3):357.

Chicago/Turabian Style

Yunchen Wang; Chunlin Huang; Yaya Feng; Minyan Zhao; Juan Gu. 2020. "Using Earth Observation for Monitoring SDG 11.3.1-Ratio of Land Consumption Rate to Population Growth Rate in Mainland China." Remote Sensing 12, no. 3: 357.

Journal article
Published: 11 December 2019 in Sustainability
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Technological changes in water use efficiency directly influence regional sustainable development. However, few studies have attempted to predict changes in water use efficiency because of the complex influencing factors and regional diversity. The Chinese Government has established a target of 0.6 for the effective utilization coefficient of irrigation water, but it is not clear how the coefficient will change in different provinces in the future. The purpose of this study is to predict irrigation water use efficiency changes using a conditional convergence model and combined with the shared socioeconomic pathways (SSPs) scenario settings and hydro-economic (HE) classification to group 31 Chinese provinces by their different economic and water resources conditions. The results show that the coefficient exponentially converges to 0.6 in half the provinces under SSP1 (sustainability), SSP2 (middle of the road), and SSP5 (conventional development) by 2030, whereas SSP3 (fragmentation) and SSP4 (inequality) are generally inefficient development pathways. HE-3 provinces (strong economic capacity, substantial hydrological challenges) achieve the greatest efficiency improvements (with all coefficients above 0.6), and SSP1 is a suitable pathway for these provinces. HE-2 provinces (strong economic capacities, low hydrological challenges) have relatively low efficiency because they lack incentives to save water, and SSP1 is also suitable for these provinces. For most HE-1 provinces (low economic capacity, low hydrological challenges), the coefficients are less than 0.6, and efforts are required to enhance their economic capacity under SSP1 or SSP5. HE-4 provinces (low economic capacity, substantial hydrological challenges) would improve efficiency in a cost-efficient manner under SSP2.

ACS Style

Aijun Guo; Daiwei Jiang; Fanglei Zhong; Xiaojiang Ding; Xiaoyu Song; Qingping Cheng; Yongnian Zhang; Chunlin Huang. Prediction of Technological Change under Shared Socioeconomic Pathways and Regional Differences: A Case Study of Irrigation Water Use Efficiency Changes in Chinese Provinces. Sustainability 2019, 11, 7103 .

AMA Style

Aijun Guo, Daiwei Jiang, Fanglei Zhong, Xiaojiang Ding, Xiaoyu Song, Qingping Cheng, Yongnian Zhang, Chunlin Huang. Prediction of Technological Change under Shared Socioeconomic Pathways and Regional Differences: A Case Study of Irrigation Water Use Efficiency Changes in Chinese Provinces. Sustainability. 2019; 11 (24):7103.

Chicago/Turabian Style

Aijun Guo; Daiwei Jiang; Fanglei Zhong; Xiaojiang Ding; Xiaoyu Song; Qingping Cheng; Yongnian Zhang; Chunlin Huang. 2019. "Prediction of Technological Change under Shared Socioeconomic Pathways and Regional Differences: A Case Study of Irrigation Water Use Efficiency Changes in Chinese Provinces." Sustainability 11, no. 24: 7103.

Journal article
Published: 21 November 2019 in Journal of Hydrology
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The performance of the Noah land surface model (LSM) with multi-parameterization options (Noah-MP) in simulating snow depth was evaluated in northern Xinjiang, China. A total number of 13,824 Noah-MP physics-ensemble simulations were conducted at the Altay site by combining different parameterization schemes of physical processes while disregarding the uncertainties of forcing data and model parameters. The natural selection approach and Tukey’s test, which are two sensitivity analysis methods, were used to analyze the sensitivity of snow to parameterization schemes. Then, the uncertainty intervals of the ensemble simulation experiments were compared. According to the results of the sensitivity and uncertainty experiments, snow depth could be simulated by three typical combination schemes at the regional scale: the longest snow melting time scheme (LT), the shortest snow melting time scheme (ST) and the default combination scheme (DT). Observation data of snow depth from thirty-nine meteorological stations in northern Xinjiang were used to evaluate the snow simulation performance of typical combination schemes. The simulation performances of the three typical combination schemes were examined and compared in groups that were divided according to elevation and land cover. The results demonstrated that the simulation results of snow depth and snow water equivalent (SWE) were sensitive to four of the eleven physics options within Noah-MP. The exclusion of the parameterization schemes that notably reduced the simulation performance in the sensitive physical processes can significantly reduce uncertainty. Snow simulation performances of three typical combination schemes were diverse in northern Xinjiang, China; no single scheme performed best at all sites, but the length of the snow melting phase exhibited the best performance.

ACS Style

Yuanhong You; Chunlin Huang; Juan Gu; Hongyi Li; Xiaohua Hao; Jinliang Hou. Assessing snow simulation performance of typical combination schemes within Noah-MP in northern Xinjiang, China. Journal of Hydrology 2019, 581, 124380 .

AMA Style

Yuanhong You, Chunlin Huang, Juan Gu, Hongyi Li, Xiaohua Hao, Jinliang Hou. Assessing snow simulation performance of typical combination schemes within Noah-MP in northern Xinjiang, China. Journal of Hydrology. 2019; 581 ():124380.

Chicago/Turabian Style

Yuanhong You; Chunlin Huang; Juan Gu; Hongyi Li; Xiaohua Hao; Jinliang Hou. 2019. "Assessing snow simulation performance of typical combination schemes within Noah-MP in northern Xinjiang, China." Journal of Hydrology 581, no. : 124380.

Journal article
Published: 04 July 2019 in Sustainability
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:A precise multi-scenario prediction of future population, based on micro-scale census data and localized interpretation of global scenarios, is significant for understanding long-term demographic changes. However, the data used in previous research need to be further refined. Few studies have focused on predicting the sex ratio at birth, which is vitally important for estimating the future size and structure of the population. It is also important to interpret and set parameters for China's future population development in line with the framework for global shared socioeconomic pathways. This paper, therefore, used the structural population data for provinces, prefectures, and counties from the Sixth National Population Census of China. It comprehensively considered the impact of China’s economic development level, specific population policies, and loss of an only child on key parameters, and localized the population change parameters for different scenarios. A population–development–environment model was used to explain the population change parameters. The population of 340 districts was refined, forecast, and aggregated to the national scale. The results show that the Chinese population is expected to first increase then decrease under the five paths from 2010 to 2050. The aging demographic structure is not reversed under any paths, and the increase or decrease in the urban and rural populations between adjacent node years is closely related to the fertility rate and urbanization speed. We suggest that measures should be taken to encourage childbearing, manage the aging population problem, and reduce the pressure on young and middle-aged people.

ACS Style

Aijun Guo; Xiaojiang Ding; Fanglei Zhong; Qingping Cheng; Chunlin Huang. Predicting the Future Chinese Population using Shared Socioeconomic Pathways, the Sixth National Population Census, and a PDE Model. Sustainability 2019, 11, 3686 .

AMA Style

Aijun Guo, Xiaojiang Ding, Fanglei Zhong, Qingping Cheng, Chunlin Huang. Predicting the Future Chinese Population using Shared Socioeconomic Pathways, the Sixth National Population Census, and a PDE Model. Sustainability. 2019; 11 (13):3686.

Chicago/Turabian Style

Aijun Guo; Xiaojiang Ding; Fanglei Zhong; Qingping Cheng; Chunlin Huang. 2019. "Predicting the Future Chinese Population using Shared Socioeconomic Pathways, the Sixth National Population Census, and a PDE Model." Sustainability 11, no. 13: 3686.

Journal article
Published: 01 July 2019 in Earth and Space Science
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Extreme precipitation over drylands, especially deserts, has been often observed. The precipitation changes in Chinese deserts have been rarely studied. Here, we use a daily grid precipitation dataset generated via weather station data (0.25° horizontal grid spacing) to investigate the spatial and temporal changes in extreme precipitation in Chinese deserts. The extreme precipitation based on the changes in the total precipitation (PRCPTOT) and the annual‐maximum daily precipitation (Rx1day) in the Chinese desert exhibits markedly increasing trends and presents a spatial distribution of wetting in the western deserts and drying in the eastern deserts. The increase in extreme precipitation could minimize wind erosion and intensify dune stabilization in the western Chinese deserts.

ACS Style

Guoshuai Li; Hong Yang; Ying Zhang; Chunlin Huang; Xiaoduo Pan; Mingguo Ma; Minhong Song; Haipeng Zhao. More Extreme Precipitation in Chinese Deserts From 1960 to 2018. Earth and Space Science 2019, 6, 1196 -1204.

AMA Style

Guoshuai Li, Hong Yang, Ying Zhang, Chunlin Huang, Xiaoduo Pan, Mingguo Ma, Minhong Song, Haipeng Zhao. More Extreme Precipitation in Chinese Deserts From 1960 to 2018. Earth and Space Science. 2019; 6 (7):1196-1204.

Chicago/Turabian Style

Guoshuai Li; Hong Yang; Ying Zhang; Chunlin Huang; Xiaoduo Pan; Mingguo Ma; Minhong Song; Haipeng Zhao. 2019. "More Extreme Precipitation in Chinese Deserts From 1960 to 2018." Earth and Space Science 6, no. 7: 1196-1204.

Journal article
Published: 12 April 2019 in Remote Sensing
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After a destructive earthquake, most of the casualties are brought about by building collapse. Our work is focused on using a single postevent PolSAR (full-polarimetric synthetic aperture radar) imagery to extract the building damage information for effective emergency decision-making. PolSAR data is subject to sunlight and contains richer backscatter information. The undamaged buildings whose orientation is not parallel to the SAR flight pass and the collapsed buildings share similar dominated scattering mechanisms, i.e., volume scattering, so they are easily confused. However, the two kinds of buildings have different textures. For a more accurate classification of damaged buildings and undamaged buildings, the OPCE (optimization of polarimetric contrast enhancement) algorithm is employed to enhance the contrast ratio of the textures for the two kinds of buildings and the precision-weighted multifeature fusion (PWMF) method is proposed to merge the multiple texture features. The experiment results show that the accuracy of the proposed novel method is improved by 8.34% compared to the traditional method. In general, the proposed PWMF method can effectively merge the multiple features and the overestimation of the building collapse rate can be reduced using the proposed method in this study.

ACS Style

Wei Zhai; Chunlin Huang; Wansheng Pei. Building Damage Assessment Based on the Fusion of Multiple Texture Features Using a Single Post-Earthquake PolSAR Image. Remote Sensing 2019, 11, 897 .

AMA Style

Wei Zhai, Chunlin Huang, Wansheng Pei. Building Damage Assessment Based on the Fusion of Multiple Texture Features Using a Single Post-Earthquake PolSAR Image. Remote Sensing. 2019; 11 (8):897.

Chicago/Turabian Style

Wei Zhai; Chunlin Huang; Wansheng Pei. 2019. "Building Damage Assessment Based on the Fusion of Multiple Texture Features Using a Single Post-Earthquake PolSAR Image." Remote Sensing 11, no. 8: 897.