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Dr. Xuecao Li
China Agricultural University

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0 Time Series Analysis
0 Urban Remote Sensing
0 Phenology
0 Urban growth modeling
0 Global Urbanization

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Phenology
Global Urbanization
Urban growth modeling

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Journal article
Published: 03 August 2021 in Agricultural and Forest Meteorology
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Phenological transitions determine the timing of changes in land surface properties and the seasonality of exchanges of biosphere-atmosphere energy, water, and carbon. Accurate mechanistic modeling of phenological processes is therefore critical to understand and correctly predict terrestrial ecosystem feedbacks with changing atmospheric and climate conditions. However, the phenological components in the land model of the US Department of Energy's (DOE) Energy Exascale Earth System Model (ELM of E3SM) were previously unable to accurately capture the observed phenological responses to environmental conditions in a well-studied boreal peatland forest. In this research, we introduced new seasonal-deciduous phenology schemes into version 1.0 of ELM and evaluated their performance against the PhenoCam observations at the Spruce and Peatland Responses Under Changing Environments (SPRUCE) experiment in northern Minnesota from 2015 to 2018. We found that phenology simulated by the revised ELM (i.e., earlier spring onsets and stronger warming responses of spring onsets and autumn senescence) was closer to observations than simulations from the original algorithms for both the deciduous conifer (Larix laricina) and mixed shrub layers. Moreover, the revised ELM generally produced higher carbon and water fluxes (e.g., photosynthesis and evapotranspiration) during the growing season and stronger flux responses to warming than the default ELM. A parameter sensitivity analysis further indicated the significant contribution of phenology parameters to uncertainty in key carbon and water cycle variables, underscoring the importance of precise phenology parameterization. This phenological modeling effort demonstrates the potential to enhance the E3SM representation of land-climate interactions at broader spatiotemporal scales, especially under anticipated elevated CO2 and warming conditions.

ACS Style

Lin Meng; Jiafu Mao; Daniel M. Ricciuto; Xiaoying Shi; Andrew D. Richardson; Paul J Hanson; Jeffrey M. Warren; Yuyu Zhou; Xuecao Li; Li Zhang; Christina Schädel. Evaluation and modification of ELM seasonal deciduous phenology against observations in a southern boreal peatland forest. Agricultural and Forest Meteorology 2021, 308-309, 108556 .

AMA Style

Lin Meng, Jiafu Mao, Daniel M. Ricciuto, Xiaoying Shi, Andrew D. Richardson, Paul J Hanson, Jeffrey M. Warren, Yuyu Zhou, Xuecao Li, Li Zhang, Christina Schädel. Evaluation and modification of ELM seasonal deciduous phenology against observations in a southern boreal peatland forest. Agricultural and Forest Meteorology. 2021; 308-309 ():108556.

Chicago/Turabian Style

Lin Meng; Jiafu Mao; Daniel M. Ricciuto; Xiaoying Shi; Andrew D. Richardson; Paul J Hanson; Jeffrey M. Warren; Yuyu Zhou; Xuecao Li; Li Zhang; Christina Schädel. 2021. "Evaluation and modification of ELM seasonal deciduous phenology against observations in a southern boreal peatland forest." Agricultural and Forest Meteorology 308-309, no. : 108556.

Journal article
Published: 23 July 2021 in Remote Sensing
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Black soil is one of the most productive soils with high organic matter content. Crop residue covering is important for protecting black soil from alleviating soil erosion and increasing soil organic carbon. Mapping crop residue covered areas accurately using remote sensing images can monitor the protection of black soil in regional areas. Considering the inhomogeneity and randomness, resulting from human management difference, the high spatial resolution Chinese GF-1 B/D image and developed MSCU-net+C deep learning method are used to mapping corn residue covered area (CRCA) in this study. The developed MSCU-net+C is joined by a multiscale convolution group (MSCG), the global loss function, and Convolutional Block Attention Module (CBAM) based on U-net and the full connected conditional random field (FCCRF). The effectiveness of the proposed MSCU-net+C is validated by the ablation experiment and comparison experiment for mapping CRCA in Lishu County, Jilin Province, China. The accuracy assessment results show that the developed MSCU-net+C improve the CRCA classification accuracy from IOUAVG = 0.8604 and KappaAVG = 0.8864 to IOUAVG = 0.9081 and KappaAVG = 0.9258 compared with U-net. Our developed and other deep semantic segmentation networks (MU-net, GU-net, MSCU-net, SegNet, and Dlv3+) improve the classification accuracy of IOUAVG/KappaAVG with 0.0091/0.0058, 0.0133/0.0091, 0.044/0.0345, 0.0104/0.0069, and 0.0107/0.0072 compared with U-net, respectively. The classification accuracies of IOUAVG/KappaAVG of traditional machine learning methods, including support vector machine (SVM) and neural network (NN), are 0.576/0.5526 and 0.6417/0.6482, respectively. These results reveal that the developed MSCU-net+C can be used to map CRCA for monitoring black soil protection.

ACS Style

Wancheng Tao; Zixuan Xie; Ying Zhang; Jiayu Li; Fu Xuan; Jianxi Huang; Xuecao Li; Wei Su; DongQin Yin. Corn Residue Covered Area Mapping with a Deep Learning Method Using Chinese GF-1 B/D High Resolution Remote Sensing Images. Remote Sensing 2021, 13, 2903 .

AMA Style

Wancheng Tao, Zixuan Xie, Ying Zhang, Jiayu Li, Fu Xuan, Jianxi Huang, Xuecao Li, Wei Su, DongQin Yin. Corn Residue Covered Area Mapping with a Deep Learning Method Using Chinese GF-1 B/D High Resolution Remote Sensing Images. Remote Sensing. 2021; 13 (15):2903.

Chicago/Turabian Style

Wancheng Tao; Zixuan Xie; Ying Zhang; Jiayu Li; Fu Xuan; Jianxi Huang; Xuecao Li; Wei Su; DongQin Yin. 2021. "Corn Residue Covered Area Mapping with a Deep Learning Method Using Chinese GF-1 B/D High Resolution Remote Sensing Images." Remote Sensing 13, no. 15: 2903.

Journal article
Published: 05 July 2021 in GIScience & Remote Sensing
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ACS Style

Xuecao Li; Jie Zhang; Zhouyuan Li; Tengyun Hu; Qiusheng Wu; Jun Yang; Jianxi Huang; Wei Su; Yuanyuan Zhao; Yuyu Zhou; Xiaoping Liu; Peng Gong; Xi Wang. Critical role of temporal contexts in evaluating urban cellular automata models. GIScience & Remote Sensing 2021, 1 -13.

AMA Style

Xuecao Li, Jie Zhang, Zhouyuan Li, Tengyun Hu, Qiusheng Wu, Jun Yang, Jianxi Huang, Wei Su, Yuanyuan Zhao, Yuyu Zhou, Xiaoping Liu, Peng Gong, Xi Wang. Critical role of temporal contexts in evaluating urban cellular automata models. GIScience & Remote Sensing. 2021; ():1-13.

Chicago/Turabian Style

Xuecao Li; Jie Zhang; Zhouyuan Li; Tengyun Hu; Qiusheng Wu; Jun Yang; Jianxi Huang; Wei Su; Yuanyuan Zhao; Yuyu Zhou; Xiaoping Liu; Peng Gong; Xi Wang. 2021. "Critical role of temporal contexts in evaluating urban cellular automata models." GIScience & Remote Sensing , no. : 1-13.

Journal article
Published: 22 June 2021 in Remote Sensing
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With rapid urbanization in recent decades, more and more urban renewal has taken place in China. Meanwhile, the early developed areas without change have become old towns, which need special attention in future city planning. However, other than field surveys, there is no specific method to identify old towns. To fill this gap, we used time-series image stacks established from Landsat Surface Reflectance Tier 1 data on the Google Earth Engine (GEE) platform, facilitated by Global Urban Boundary (GUB), Essential Urban Land Use Categories (EULUC) and Global Artificial Impervious Area (GAIA) data. The LandTrendr change detection algorithm was applied to extract detailed information from 14 band/index trajectories. These features were then used as inputs to two methods of old town identification: statistical thresholding and random forest classification. We assessed these two methods in a rapidly developing large city, Hangzhou, and subsequently obtained overall accuracies of 81.33% and 90.67%, respectively. Red band, NIR band and related indices show higher importance in random forest classification, and the magnitude feature plays an outstanding role. The final map of Hangzhou during the 2000–2018 period shows that the old towns were concentrated in the downtown region near West Lake within the urban boundaries in 2000, and far fewer than the renewed areas. The results could serve as references in the provincial and national planning of future urban developments.

ACS Style

Hao Ni; Peng Gong; Xuecao Li. Extraction of Old Towns in Hangzhou (2000–2018) from Landsat Time Series Image Stacks. Remote Sensing 2021, 13, 2438 .

AMA Style

Hao Ni, Peng Gong, Xuecao Li. Extraction of Old Towns in Hangzhou (2000–2018) from Landsat Time Series Image Stacks. Remote Sensing. 2021; 13 (13):2438.

Chicago/Turabian Style

Hao Ni; Peng Gong; Xuecao Li. 2021. "Extraction of Old Towns in Hangzhou (2000–2018) from Landsat Time Series Image Stacks." Remote Sensing 13, no. 13: 2438.

Research article
Published: 17 June 2021 in GIScience & Remote Sensing
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High spatiotemporal population data are critical for a wide range of applications (e.g. urban planning and management, risk assessment, and epidemic control). However, such data are still not widely available due to the limited knowledge of complex human activities. Here we proposed a spatiotemporal downscaling framework for estimating hourly population dynamics in Beijing by integrating remote sensing and social sensing data. First, we generated two baseline maps of population during sleep and work times using a dasymetric method. Second, we generated urban functional zones using a random forest model and derived human activity patterns from social sensing data. Finally, we estimated the hourly population dynamics at a 500-meter resolution using a temporal downscaling method. Results show the significant spatial difference of the population over time, especially between working hours (9:00 − 18:00) and sleeping hours (after 0:00). The spatial pattern of population is more homogenous within the sixth ring area in Beijing during work time compared to sleep time when there are more clusters of high population. The comparison of spatiotemporal patterns with the referenced real-time heat maps from Baidu indicates that our population data are reliable. The framework presented in this paper is transferable in other regions. The resulting dataset of hourly population dynamics is of great help for governments of emergency responses as well as for studies about human risks to environmental issues.

ACS Style

Xia Zhao; Yuyu Zhou; Wei Chen; Xi Li; Xuecao Li; Deren Li. Mapping hourly population dynamics using remotely sensed and geospatial data: a case study in Beijing, China. GIScience & Remote Sensing 2021, 58, 717 -732.

AMA Style

Xia Zhao, Yuyu Zhou, Wei Chen, Xi Li, Xuecao Li, Deren Li. Mapping hourly population dynamics using remotely sensed and geospatial data: a case study in Beijing, China. GIScience & Remote Sensing. 2021; 58 (5):717-732.

Chicago/Turabian Style

Xia Zhao; Yuyu Zhou; Wei Chen; Xi Li; Xuecao Li; Deren Li. 2021. "Mapping hourly population dynamics using remotely sensed and geospatial data: a case study in Beijing, China." GIScience & Remote Sensing 58, no. 5: 717-732.

Journal article
Published: 09 May 2021 in Remote Sensing
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Light pollution, a phenomenon in which artificial nighttime light (NTL) changes the form of brightness and darkness in natural areas such as protected areas (PAs), has become a global concern due to its threat to global biodiversity. With ongoing global urbanization and climate change, the light pollution status in global PAs deserves attention for mitigation and adaptation. In this study, we developed a framework to evaluate the light pollution status in global PAs, using the global NTL time series data. First, we classified global PAs (30,624) into three pollution categories: non-polluted (5974), continuously polluted (8141), and discontinuously polluted (16,509), according to the time of occurrence of lit pixels in/around PAs from 1992 to 2018. Then, we explored the NTL intensity (e.g., digital numbers) and its trend in those polluted PAs and identified those hotspots of PAs at the global scale with consideration of global urbanization. Our study shows that global light pollution is mainly distributed within the range of 30°N and 60°N, including Europe, north America, and East Asia. Although the temporal trend of NTL intensity in global PAs is increasing, Japan and the United States of America (USA) have opposite trends due to the implementation of well-planned ecological conservation policies and declining population growth. For most polluted PAs, the lit pixels are close to their boundaries (i.e., less than 10 km), and the NTL in/around these lit areas has become stronger over the past decades. The identified hotspots of PAs (e.g., Europe, the USA, and East Asia) help support decisions on global biodiversity conservation, particularly with global urbanization and climate change.

ACS Style

Haowei Mu; Xuecao Li; Xiaoping Du; Jianxi Huang; Wei Su; Tengyun Hu; Yanan Wen; Peiyi Yin; Yuan Han; Fei Xue. Evaluation of Light Pollution in Global Protected Areas from 1992 to 2018. Remote Sensing 2021, 13, 1849 .

AMA Style

Haowei Mu, Xuecao Li, Xiaoping Du, Jianxi Huang, Wei Su, Tengyun Hu, Yanan Wen, Peiyi Yin, Yuan Han, Fei Xue. Evaluation of Light Pollution in Global Protected Areas from 1992 to 2018. Remote Sensing. 2021; 13 (9):1849.

Chicago/Turabian Style

Haowei Mu; Xuecao Li; Xiaoping Du; Jianxi Huang; Wei Su; Tengyun Hu; Yanan Wen; Peiyi Yin; Yuan Han; Fei Xue. 2021. "Evaluation of Light Pollution in Global Protected Areas from 1992 to 2018." Remote Sensing 13, no. 9: 1849.

Cover image
Published: 13 March 2021 in Diversity and Distributions
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The cover image relates to the Research Article https://doi.org/10.1111/ddi.13225 “Lineage‐level distribution models lead to more realistic climate change predictions for a threatened crayfi sh” by Zhixin Zhang et al. The photo (taken in 2009) shows an eastern lineage Japanese crayfi sh (Cambaroides japonicus) in a small woodland stream in Hokkaido, Japan. Photo credit: Nisikawa Usio.

ACS Style

Zhixin Zhang; Jamie M. Kass; Stefano Mammola; Itsuro Koizumi; Xuecao Li; Kazunori Tanaka; Kousuke Ikeda; Toru Suzuki; Masashi Yokota; Nisikawa Usio. Front Cover. Diversity and Distributions 2021, 27, 1 .

AMA Style

Zhixin Zhang, Jamie M. Kass, Stefano Mammola, Itsuro Koizumi, Xuecao Li, Kazunori Tanaka, Kousuke Ikeda, Toru Suzuki, Masashi Yokota, Nisikawa Usio. Front Cover. Diversity and Distributions. 2021; 27 (4):1.

Chicago/Turabian Style

Zhixin Zhang; Jamie M. Kass; Stefano Mammola; Itsuro Koizumi; Xuecao Li; Kazunori Tanaka; Kousuke Ikeda; Toru Suzuki; Masashi Yokota; Nisikawa Usio. 2021. "Front Cover." Diversity and Distributions 27, no. 4: 1.

Journal article
Published: 29 January 2021 in Remote Sensing
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Urban land use mapping is critical to understanding human activities in space. The first national mapping result of essential urban land use categories of China (EULUC-China) was released in 2019. However, the overall accuracies in some of the plain cities such as Beijing, Chengdu, and Zhengzhou were lower than 50% because many parcel-based mapping units are large with mixed land uses. To address this shortcoming, we proposed an area of interest (AOI)-based mapping approach, choosing Beijing as our study area. The mapping process includes two major steps. First, grids with different sizes (i.e., 300 m, 200 m, and 100 m) were derived from original land parcels to obtain classification units with a suitable size. Then, features within these grids were extracted from Sentinel-2 spectral data, point of interest (POI), and Tencent Easygo crowdedness data. These features were classified using a random forest (RF) classifier with AOI data, resulting in a 10-category map of EULUC. Second, we superimposed the AOIs layer on classified units to do some rectification and offer more details at the building scale. The overall accuracy of the AOI layer reached 98%, and the overall accuracy of the mapping results reached 77%. This study provides a fast method for accurate geographic sample collection, which substantially reduces the amount of fieldwork for sample collection and improves the classification accuracy compared to previous EULUC mapping. The detailed urban land use map could offer more support for urban planning and environmental policymaking.

ACS Style

Xiaoting Li; Tengyun Hu; Peng Gong; Shihong Du; Bin Chen; Xuecao Li; Qi Dai. Mapping Essential Urban Land Use Categories in Beijing with a Fast Area of Interest (AOI)-Based Method. Remote Sensing 2021, 13, 477 .

AMA Style

Xiaoting Li, Tengyun Hu, Peng Gong, Shihong Du, Bin Chen, Xuecao Li, Qi Dai. Mapping Essential Urban Land Use Categories in Beijing with a Fast Area of Interest (AOI)-Based Method. Remote Sensing. 2021; 13 (3):477.

Chicago/Turabian Style

Xiaoting Li; Tengyun Hu; Peng Gong; Shihong Du; Bin Chen; Xuecao Li; Qi Dai. 2021. "Mapping Essential Urban Land Use Categories in Beijing with a Fast Area of Interest (AOI)-Based Method." Remote Sensing 13, no. 3: 477.

Biodiversity research
Published: 21 January 2021 in Diversity and Distributions
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Aim As climate change presents a major threat to biodiversity in the next decades, it is critical to assess its impact on species habitat suitability to inform biodiversity conservation. Species distribution models (SDMs) are a widely used tool to assess climate change impacts on species’ geographical distributions. As the name of these models suggests, the species level is the most commonly used taxonomic unit in SDMs. However, recently it has been demonstrated that SDMs considering taxonomic resolution below (or above) the species level can make more reliable predictions of biodiversity change when different populations exhibit local adaptation. Here, we tested this idea using the Japanese crayfish (Cambaroides japonicus), a threatened species encompassing two geographically structured and phylogenetically distinct genetic lineages. Location Northern Japan. Methods We first estimated niche differentiation between the two lineages of C. japonicus using n‐dimensional hypervolumes and then made climate change predictions of habitat suitability using SDMs constructed at two phylogenetic levels: species and intraspecific lineage. Results Our results showed only intermediate niche overlap, demonstrating measurable niche differences between the two lineages. The species‐level SDM made future predictions that predicted much broader and severe impacts of climate change. However, the lineage‐level SDMs led to reduced climate change impacts overall and also suggested that the eastern lineage may be more resilient to climate change than the western one. Main conclusions The two lineages of C. japonicus occupy different niche spaces. Compared with lineage‐level models, species‐level models can overestimate climate change impacts. These results not only have important implications for designing future conservation strategies for this threatened species, but also highlight the need for incorporating genetic information into SDMs to obtain realistic predictions of biodiversity change.

ACS Style

Zhixin Zhang; Jamie M. Kass; Stefano Mammola; Itsuro Koizumi; Xuecao Li; Kazunori Tanaka; Kousuke Ikeda; Toru Suzuki; Masashi Yokota; Nisikawa Usio. Lineage‐level distribution models lead to more realistic climate change predictions for a threatened crayfish. Diversity and Distributions 2021, 27, 684 -695.

AMA Style

Zhixin Zhang, Jamie M. Kass, Stefano Mammola, Itsuro Koizumi, Xuecao Li, Kazunori Tanaka, Kousuke Ikeda, Toru Suzuki, Masashi Yokota, Nisikawa Usio. Lineage‐level distribution models lead to more realistic climate change predictions for a threatened crayfish. Diversity and Distributions. 2021; 27 (4):684-695.

Chicago/Turabian Style

Zhixin Zhang; Jamie M. Kass; Stefano Mammola; Itsuro Koizumi; Xuecao Li; Kazunori Tanaka; Kousuke Ikeda; Toru Suzuki; Masashi Yokota; Nisikawa Usio. 2021. "Lineage‐level distribution models lead to more realistic climate change predictions for a threatened crayfish." Diversity and Distributions 27, no. 4: 684-695.

Letter
Published: 28 October 2020 in Remote Sensing
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Garlic and winter wheat are major economic and grain crops in China, and their boundaries have increased substantially in recent decades. Updated and accurate garlic and winter wheat maps are critical for assessing their impacts on society and the environment. Remote sensing imagery can be used to monitor spatial and temporal changes in croplands such as winter wheat and maize. However, to our knowledge, few studies are focusing on garlic area mapping. Here, we proposed a method for coupling active and passive satellite imagery for the identification of both garlic and winter wheat in Northern China. First, we used passive satellite imagery (Sentinel-2 and Landsat-8 images) to extract winter crops (garlic and winter wheat) with high accuracy. Second, we applied active satellite imagery (Sentinel-1 images) to distinguish garlic from winter wheat. Third, we generated a map of the garlic and winter wheat by coupling the above two classification results. For the evaluation of classification, the overall accuracy was 95.97%, with a kappa coefficient of 0.94 by eighteen validation quadrats (3 km by 3 km). The user’s and producer’s accuracies of garlic are 95.83% and 95.85%, respectively; and for the winter wheat, these two accuracies are 97.20% and 97.45%, respectively. This study provides a practical exploration of targeted crop identification in mixed planting areas using multisource remote sensing data.

ACS Style

Haifeng Tian; Jie Pei; Jianxi Huang; Xuecao Li; Jian Wang; Boyan Zhou; Yaochen Qin; Li Wang. Garlic and Winter Wheat Identification Based on Active and Passive Satellite Imagery and the Google Earth Engine in Northern China. Remote Sensing 2020, 12, 3539 .

AMA Style

Haifeng Tian, Jie Pei, Jianxi Huang, Xuecao Li, Jian Wang, Boyan Zhou, Yaochen Qin, Li Wang. Garlic and Winter Wheat Identification Based on Active and Passive Satellite Imagery and the Google Earth Engine in Northern China. Remote Sensing. 2020; 12 (21):3539.

Chicago/Turabian Style

Haifeng Tian; Jie Pei; Jianxi Huang; Xuecao Li; Jian Wang; Boyan Zhou; Yaochen Qin; Li Wang. 2020. "Garlic and Winter Wheat Identification Based on Active and Passive Satellite Imagery and the Google Earth Engine in Northern China." Remote Sensing 12, no. 21: 3539.

Journal article
Published: 23 October 2020 in Sensors
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Green leaf area index (LAI) is an important variable related to crop growth. Accurate and timely information on LAI is essential for developing suitable field management strategies to mitigate risk and boost yield. Several remote sensing (RS) based methods have been recently developed to estimate LAI at the regional scale. However, the performance of these methods tends to be affected by the quality of RS data, especially when time-series LAI are required. For crop LAI estimation, supplementary growth information from crop model is helpful to address this issue. In this study, we focus on the regional-scale LAI estimations of spring maize for the entire growth season. Using time-series multispectral RS data acquired by an unmanned aerial vehicle (UAV) and the World Food Studies (WOFOST) crop model, three methods were applied at different crop growth stages: empirical method using vegetation index (VI), data assimilation method and hybrid method. The VI-based method and assimilation method were used to generate time-series LAI estimations for the whole crop growth season. Then, a hybrid method specially for the late-stage LAI retrieval was developed by integrating WOFOST model and data assimilation. Using field-collected LAI data in Hongxing Farm in 2014, the performances of these three methods were evaluated. At the early stage, the VI-based method (R2 = 0.63, RMSE = 0.16, n = 36) achieved higher accuracy than the assimilation method (R2 = 0.54, RMSE = 0.52, n = 36), whereas at the mid stage, the assimilation method (R2 = 0.63, RMSE = 0.46, n = 28) showed higher accuracy than the VI-based method (R2 = 0.41, RMSE = 0.51, n = 28). At the late stage, the hybrid method yielded the highest accuracy (R2 = 0.63, RMSE = 0.46, n = 29), compared with the VI-based method (R2 = 0.19, RMSE = 0.43, n = 28) and the assimilation method (R2 = 0.20, RMSE = 0.44, n = 29). Based on the results above, we considered a combination of the three methods, i.e., the VI-based method for the early stage, the assimilation method for the mid stage, and the hybrid method for the late stage, as an ideal strategy for spring-maize LAI estimation for the entire growth season of 2014 in Hongxing Farm, and the accuracy of the combined method over the whole growth season is higher than that of any single method.

ACS Style

Zhiqiang Cheng; Jihua Meng; Jiali Shang; Jiangui Liu; Jianxi Huang; Yanyou Qiao; Budong Qian; Qi Jing; Taifeng Dong; Lihong Yu. Generating Time-Series LAI Estimates of Maize Using Combined Methods Based on Multispectral UAV Observations and WOFOST Model. Sensors 2020, 20, 6006 .

AMA Style

Zhiqiang Cheng, Jihua Meng, Jiali Shang, Jiangui Liu, Jianxi Huang, Yanyou Qiao, Budong Qian, Qi Jing, Taifeng Dong, Lihong Yu. Generating Time-Series LAI Estimates of Maize Using Combined Methods Based on Multispectral UAV Observations and WOFOST Model. Sensors. 2020; 20 (21):6006.

Chicago/Turabian Style

Zhiqiang Cheng; Jihua Meng; Jiali Shang; Jiangui Liu; Jianxi Huang; Yanyou Qiao; Budong Qian; Qi Jing; Taifeng Dong; Lihong Yu. 2020. "Generating Time-Series LAI Estimates of Maize Using Combined Methods Based on Multispectral UAV Observations and WOFOST Model." Sensors 20, no. 21: 6006.

Journal article
Published: 07 September 2020 in Remote Sensing
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Predicting crop maturity dates is important for improving crop harvest planning and grain quality. The prediction of crop maturity dates by assimilating remote sensing information into crop growth model has not been fully explored. In this study, a data assimilation framework incorporating the leaf area index (LAI) product from Moderate Resolution Imaging Spectroradiometer (MODIS) into a World Food Studies (WOFOST) model was proposed to predict the maturity dates of winter wheat in Henan province, China. Minimization of normalized cost function was used to obtain the input parameters of the WOFOST model. The WOFOST model was run with the re-initialized parameter to forecast the maturity dates of winter wheat grid by grid, and THORPEX Interactive Grand Global Ensemble (TIGGE) was used as forecasting period weather input in the future 15 days (d) for the WOFOST model. The results demonstrated a promising regional maturity date prediction with determination coefficient (R2) of 0.94 and the root mean square error (RMSE) of 1.86 d. The outcomes also showed that the optimal forecasting starting time for Henan was 30 April, corresponding to a stage from anthesis to grain filling. Our study indicated great potential of using data assimilation approaches in winter wheat maturity date prediction.

ACS Style

Wen Zhuo; Jianxi Huang; Xinran Gao; Hongyuan Ma; Hai Huang; Wei Su; Jihua Meng; Ying Li; Huailiang Chen; DongQin Yin. Prediction of Winter Wheat Maturity Dates through Assimilating Remotely Sensed Leaf Area Index into Crop Growth Model. Remote Sensing 2020, 12, 2896 .

AMA Style

Wen Zhuo, Jianxi Huang, Xinran Gao, Hongyuan Ma, Hai Huang, Wei Su, Jihua Meng, Ying Li, Huailiang Chen, DongQin Yin. Prediction of Winter Wheat Maturity Dates through Assimilating Remotely Sensed Leaf Area Index into Crop Growth Model. Remote Sensing. 2020; 12 (18):2896.

Chicago/Turabian Style

Wen Zhuo; Jianxi Huang; Xinran Gao; Hongyuan Ma; Hai Huang; Wei Su; Jihua Meng; Ying Li; Huailiang Chen; DongQin Yin. 2020. "Prediction of Winter Wheat Maturity Dates through Assimilating Remotely Sensed Leaf Area Index into Crop Growth Model." Remote Sensing 12, no. 18: 2896.

Journal article
Published: 13 August 2020 in Remote Sensing
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The Guangdong–Hong Kong–Macau Greater Bay Area (GBA) of China is one of the largest bay areas in the world. However, the spatiotemporal characteristics and driving mechanisms of urban expansions in this region are poorly understood. Here we used the annual remote sensing images, Geographic Information System (GIS) techniques, and geographical detector method to characterize the spatiotemporal patterns of urban expansion in the GBA and investigate their driving factors during 1986–2017 on regional and city scales. The results showed that: the GBA experienced an unprecedented urban expansion over the past 32 years. The total urban area expanded from 652.74 km2 to 8137.09 km2 from 1986 to 2017 (approximately 13 times). The annual growth rate during 1986–2017 was 8.20% and the annual growth rate from 1986 to 1990 was the highest (16.89%). Guangzhou, Foshan, Dongguan, and Shenzhen experienced the highest urban expansion rate, with the annual increase of urban areas in 51.51, 45.54, 36.76, and 23.26 km2 y−1, respectively, during 1986–2017. Gross Domestic Product (GDP), income, road length, and population were the most important driving factors of the urban expansions in the GBA. We also found the driving factors of the urban expansions varied with spatial and temporal scales, suggesting the general understanding from the regional level may not reveal detailed urban dynamics. Detailed urban management and planning policies should be made considering the spatial and internal heterogeneity. These findings can enhance the comprehensive understanding of this bay area and help policymakers to promote sustainable development in the future.

ACS Style

Jie Zhang; Le Yu; Xuecao Li; Chenchen Zhang; Tiezhu Shi; Xiangyin Wu; Chao Yang; Wenxiu Gao; Qingquan Li; Guofeng Wu. Exploring Annual Urban Expansions in the Guangdong-Hong Kong-Macau Greater Bay Area: Spatiotemporal Features and Driving Factors in 1986–2017. Remote Sensing 2020, 12, 2615 .

AMA Style

Jie Zhang, Le Yu, Xuecao Li, Chenchen Zhang, Tiezhu Shi, Xiangyin Wu, Chao Yang, Wenxiu Gao, Qingquan Li, Guofeng Wu. Exploring Annual Urban Expansions in the Guangdong-Hong Kong-Macau Greater Bay Area: Spatiotemporal Features and Driving Factors in 1986–2017. Remote Sensing. 2020; 12 (16):2615.

Chicago/Turabian Style

Jie Zhang; Le Yu; Xuecao Li; Chenchen Zhang; Tiezhu Shi; Xiangyin Wu; Chao Yang; Wenxiu Gao; Qingquan Li; Guofeng Wu. 2020. "Exploring Annual Urban Expansions in the Guangdong-Hong Kong-Macau Greater Bay Area: Spatiotemporal Features and Driving Factors in 1986–2017." Remote Sensing 12, no. 16: 2615.

Articles
Published: 10 August 2020 in International Journal of Digital Earth
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We present systematic analyses of the temporal dynamics of the growth of Kumasi, the fastest growing city in Ghana using 20-year Landsat time-series data from 2000 to 2020 (with 1986 Landsat image as a baseline). Two classification algorithms – random forest (RF) and support vector machines (SVM) – were used to produce binary (built-up / non-built up) maps for all years within the temporal span. We further implemented an anomaly detection and temporal consistency algorithm followed by a changing logic to correct the classification anomalies due to image contamination from the cloud and other sources. The mean overall accuracies obtained for RF and SVM were 94.9% (kappa = 0.90) and 95.5% (kappa = 0.91), respectively. Our results reveal that the mean built-up area percentages of the metropolis are approximately 74, 65, 47, and 23 for the years 2020, 2010, 2000, and 1986, respectively, representing a mean annual change of 3.5% over the 34 years. With the present lack of labeled data in Ghana for in-depth analyses of the evolution of land use, we believe that this study serves as an initial attempt to a better understanding of the effects of increasing anthropogenic activities due to urbanization, on human and environment health.

ACS Style

Kwame O. Hackman; Xuecao Li; Daniel Asenso-Gyambibi; Emmanuella A. Asamoah; Isaac. D. Nelson. Analysis of geo-spatiotemporal data using machine learning algorithms and reliability enhancement for urbanization decision support. International Journal of Digital Earth 2020, 13, 1717 -1732.

AMA Style

Kwame O. Hackman, Xuecao Li, Daniel Asenso-Gyambibi, Emmanuella A. Asamoah, Isaac. D. Nelson. Analysis of geo-spatiotemporal data using machine learning algorithms and reliability enhancement for urbanization decision support. International Journal of Digital Earth. 2020; 13 (12):1717-1732.

Chicago/Turabian Style

Kwame O. Hackman; Xuecao Li; Daniel Asenso-Gyambibi; Emmanuella A. Asamoah; Isaac. D. Nelson. 2020. "Analysis of geo-spatiotemporal data using machine learning algorithms and reliability enhancement for urbanization decision support." International Journal of Digital Earth 13, no. 12: 1717-1732.

Journal article
Published: 24 July 2020 in Remote Sensing
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Vegetation phenology plays a pivotal role in regulating several ecological processes and has profound impacts on global carbon exchange. Large-scale vegetation phenology monitoring mostly relies on Low-Earth-Orbit satellite observations with low temporal resolutions, leaving gaps in data that are important for monitoring seasonal vegetation phenology. High temporal resolution satellite observations have the potential to fill this gap by frequently collecting observations on a global scale, making it easier to study change over time. This study explored the potential of using the Earth Polychromatic Imaging Camera (EPIC) onboard the Deep Space Climate Observatory (DSCOVR) satellite, which captures images of the entire sunlit face of the Earth at a temporal resolution of once every 1–2 h, to observe vegetation phenology cycles in North America. We assessed the strengths and shortcomings of EPIC-based phenology information in comparison with the Moderate-resolution Imaging Spectroradiometer (MODIS), Enhanced Thematic Mapper (ETM+) onboard Landsat 7, and PhenoCam ground-based observations across six different plant functional types. Our results indicated that EPIC could capture and characterize seasonal changes of vegetation across different plant functional types and is particularly consistent in the estimated growing season length. Our results also provided new insights into the complementary features and benefits of the four datasets, which is valuable for improving our understanding of the complex response of vegetation to global climate variability and other disturbances and the impact of phenology changes on ecosystem productivity and global carbon exchange.

ACS Style

Maridee Weber; Dalei Hao; Ghassem Asrar; Yuyu Zhou; Xuecao Li; Min Chen. Exploring the Use of DSCOVR/EPIC Satellite Observations to Monitor Vegetation Phenology. Remote Sensing 2020, 12, 2384 .

AMA Style

Maridee Weber, Dalei Hao, Ghassem Asrar, Yuyu Zhou, Xuecao Li, Min Chen. Exploring the Use of DSCOVR/EPIC Satellite Observations to Monitor Vegetation Phenology. Remote Sensing. 2020; 12 (15):2384.

Chicago/Turabian Style

Maridee Weber; Dalei Hao; Ghassem Asrar; Yuyu Zhou; Xuecao Li; Min Chen. 2020. "Exploring the Use of DSCOVR/EPIC Satellite Observations to Monitor Vegetation Phenology." Remote Sensing 12, no. 15: 2384.

Journal article
Published: 16 July 2020 in Remote Sensing of Environment
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The long-term urban dynamics at regional and global scales is essential to understanding the urbanization processes and environmental consequences for providing better scientific insights and effective decision-making. The time series of consistent nighttime light (NTL) data generated by integrating the Defense Meteorological Satellite Program-Operational Linescane System (DMSP-OLS) and the Suomi National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) provide a longer consistent record of the nightscape beyond a single dataset for monitoring urban dynamics. In this study, we developed a new framework based on the spatial variation of NTL gradient (SVNG) to map urban dynamics in Southeast Asia using the consistent NTL data (1992–2018). First, we identified the potential urban clusters in the region using the cluster-based segmentation approach in 2018. Second, we applied the SVNG framework in each potential urban cluster to extract the initial annual urban extent from corresponding time-series NTL images (1992–2018). Finally, we performed a temporal consistency check on the initial urban extent to obtain the final urban sequence in Southeast Asia. The evaluation on the spatiotemporal patterns and consistency of urban dynamics using other urban products indicates that the SVNG framework can effectively capture the urban dynamics in areas with different development levels and patterns. Moreover, we investigated urban dynamics in Southeast Asia at the local, national, and regional scales. This study opens new research avenues for monitoring and understanding the long-term urban dynamics and the pathways of urban growth from local to global scales.

ACS Style

Min Zhao; Yuyu Zhou; Xuecao Li; Weiming Cheng; Chenghu Zhou; Ting Ma; Manchun Li; Kun Huang. Mapping urban dynamics (1992–2018) in Southeast Asia using consistent nighttime light data from DMSP and VIIRS. Remote Sensing of Environment 2020, 248, 111980 .

AMA Style

Min Zhao, Yuyu Zhou, Xuecao Li, Weiming Cheng, Chenghu Zhou, Ting Ma, Manchun Li, Kun Huang. Mapping urban dynamics (1992–2018) in Southeast Asia using consistent nighttime light data from DMSP and VIIRS. Remote Sensing of Environment. 2020; 248 ():111980.

Chicago/Turabian Style

Min Zhao; Yuyu Zhou; Xuecao Li; Weiming Cheng; Chenghu Zhou; Ting Ma; Manchun Li; Kun Huang. 2020. "Mapping urban dynamics (1992–2018) in Southeast Asia using consistent nighttime light data from DMSP and VIIRS." Remote Sensing of Environment 248, no. : 111980.

Journal article
Published: 28 May 2020 in Remote Sensing
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Timely and accurate forecasting of crop yields is crucial to food security and sustainable development in the agricultural sector. However, winter wheat yield estimation and forecasting on a regional scale still remains challenging. In this study, we established a two-branch deep learning model to predict winter wheat yield in the main producing regions of China at the county level. The first branch of the model was constructed based on the Long Short-Term Memory (LSTM) networks with inputs from meteorological and remote sensing data. Another branch was constructed using Convolution Neural Networks (CNN) to model static soil features. The model was then trained using the detrended statistical yield data during 1982 to 2015 and evaluated by leave-one-year-out-validation. The evaluation results showed a promising performance of the model with the overall R 2 and RMSE of 0.77 and 721 kg/ha, respectively. We further conducted yield prediction and uncertainty analysis based on the two-branch model and obtained the forecast accuracy in one month prior to harvest of 0.75 and 732 kg/ha. Results also showed that while yield detrending could potentially introduce higher uncertainty, it had the advantage of improving the model performance in yield prediction.

ACS Style

Xinlei Wang; Jianxi Huang; Quanlong Feng; DongQin Yin. Winter Wheat Yield Prediction at County Level and Uncertainty Analysis in Main Wheat-Producing Regions of China with Deep Learning Approaches. Remote Sensing 2020, 12, 1744 .

AMA Style

Xinlei Wang, Jianxi Huang, Quanlong Feng, DongQin Yin. Winter Wheat Yield Prediction at County Level and Uncertainty Analysis in Main Wheat-Producing Regions of China with Deep Learning Approaches. Remote Sensing. 2020; 12 (11):1744.

Chicago/Turabian Style

Xinlei Wang; Jianxi Huang; Quanlong Feng; DongQin Yin. 2020. "Winter Wheat Yield Prediction at County Level and Uncertainty Analysis in Main Wheat-Producing Regions of China with Deep Learning Approaches." Remote Sensing 12, no. 11: 1744.

Analysis
Published: 04 May 2020 in Nature Sustainability
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High-resolution global maps of annual urban land coverage provide fundamental information of global environmental change and contribute to applications related to climate mitigation and urban planning for sustainable development. Here we map global annual urban dynamics from 1985 to 2015 at a 30 m resolution using numerous surface reflectance data from Landsat satellites. We find that global urban extent has expanded by 9,687 km2 per year. This rate is four times greater than previous reputable estimates from worldwide individual cities, suggesting an unprecedented rate of global urbanization. The rate of urban expansion is notably faster than that of population growth, indicating that the urban land area already exceeds what is needed to sustain population growth. Looking ahead, using these maps in conjunction with integrated assessment models can facilitate greater understanding of the complex environmental impacts of urbanization and help urban planners avoid natural hazards; for example, by limiting new development in flood risk zones.

ACS Style

Xiaoping Liu; Yinghuai Huang; Xiaocong Xu; Xuecao Li; Xia Li; Philippe Ciais; Peirong Lin; Kai Gong; Alan D. Ziegler; Anping Chen; Peng Gong; Jun Chen; Guohua Hu; Yimin Chen; Shaojian Wang; Qiusheng Wu; Kangning Huang; Lyndon Estes; Zhenzhong Zeng. High-spatiotemporal-resolution mapping of global urban change from 1985 to 2015. Nature Sustainability 2020, 3, 564 -570.

AMA Style

Xiaoping Liu, Yinghuai Huang, Xiaocong Xu, Xuecao Li, Xia Li, Philippe Ciais, Peirong Lin, Kai Gong, Alan D. Ziegler, Anping Chen, Peng Gong, Jun Chen, Guohua Hu, Yimin Chen, Shaojian Wang, Qiusheng Wu, Kangning Huang, Lyndon Estes, Zhenzhong Zeng. High-spatiotemporal-resolution mapping of global urban change from 1985 to 2015. Nature Sustainability. 2020; 3 (7):564-570.

Chicago/Turabian Style

Xiaoping Liu; Yinghuai Huang; Xiaocong Xu; Xuecao Li; Xia Li; Philippe Ciais; Peirong Lin; Kai Gong; Alan D. Ziegler; Anping Chen; Peng Gong; Jun Chen; Guohua Hu; Yimin Chen; Shaojian Wang; Qiusheng Wu; Kangning Huang; Lyndon Estes; Zhenzhong Zeng. 2020. "High-spatiotemporal-resolution mapping of global urban change from 1985 to 2015." Nature Sustainability 3, no. 7: 564-570.

Journal article
Published: 02 April 2020 in Sustainability
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Monitoring agricultural drought is important to food security and the sustainable development of human society. In order to improve the accuracy of soil moisture and winter wheat yield estimation, drought monitoring effects of optical drought index data, meteorological drought data, and passive microwave soil moisture data were explored during individual and whole growth periods of winter wheat in 2003–2011, taking Henan Province of China as the research area. The model of drought indices and relative meteorological yield of winter wheat in individual and whole growth periods was constructed based on multiple linear regression. Results showed a higher correlation between Moderate-Resolution Imaging Spectroradiometer (MODIS) drought indices and 10 cm relative soil moisture (RSM10) than 20 cm (RSM20) and 50 cm (RSM50). In the whole growth period, the correlation coefficient (R) between vegetation supply water index (VSWI) and RSM10 had the highest correlation (R = −0.206), while in individual growth periods, the vegetation temperature condition index (VTCI) was superior to the vegetation health index (VHI) and VSWI. Among the meteorological drought indices, the 10-day, 20-day, and 30-day standard precipitation evapotranspiration indices (SPEI1, SPEI2, and SPEI3) were all most relevant to RSM10 during individual and whole growth periods. RSM50 and SPEI3 had a higher correlation, indicating that deep soil moisture was more related to drought on a long time scale. The relationship between Advanced Microwave Scanning Radiometer for EOS soil moisture (AMSR-E SM) and VTCI was stable and significantly positive in individual and whole growth periods, which was better compared to VHI and VSWI. Compared with the drought indices and the relative meteorological yield in the city, VHI had the best monitoring effect during individual and whole growth periods. Results also showed that drought occurring at the jointing–heading stage can reduce winter wheat yield, while a certain degree of drought occurring at the heading–milk ripening stage can increase the yield. In the whole growth period, the combination of SPEI1, SPEI2, and VHI had the best performance, with a coefficient of determination (R2) of 0.282 with the combination of drought indices as the independent variables and relative meteorological yield as the dependent variable. In the individual growth period, the model in the later growth period of winter wheat performed well, especially in the returning green–jointing stage (R2 = 0.212). Results show that the combination of multiple linear drought indices in the whole growth period and the model in the returning green–jointing period could improve the accuracy of winter wheat yield estimation. This study is helpful for effective agricultural drought monitoring of winter wheat in Henan Province.

ACS Style

Yuan Li; Yi Dong; DongQin Yin; Diyou Liu; Pengxin Wang; Jianxi Huang; Zhe Liu; Hongshuo Wang. Evaluation of Drought Monitoring Effect of Winter Wheat in Henan Province of China Based on Multi-Source Data. Sustainability 2020, 12, 2801 .

AMA Style

Yuan Li, Yi Dong, DongQin Yin, Diyou Liu, Pengxin Wang, Jianxi Huang, Zhe Liu, Hongshuo Wang. Evaluation of Drought Monitoring Effect of Winter Wheat in Henan Province of China Based on Multi-Source Data. Sustainability. 2020; 12 (7):2801.

Chicago/Turabian Style

Yuan Li; Yi Dong; DongQin Yin; Diyou Liu; Pengxin Wang; Jianxi Huang; Zhe Liu; Hongshuo Wang. 2020. "Evaluation of Drought Monitoring Effect of Winter Wheat in Henan Province of China Based on Multi-Source Data." Sustainability 12, no. 7: 2801.

Preprint content
Published: 23 March 2020
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Changes in urban environments play important roles in sustainable urban development. Satellite observations in fine spatial and temporal resolutions, together with new computer technologies, provide the possibility to monitor these changes across large geographic areas and over a long time period. In this study, we developed new algorithms to characterize dynamics of urban extent, urban heat island, and phenology (i.e., onsets of green-up and senescence phases) and successfully implemented them on the advanced Google Earth Engine, a start-of-art platform for planetary-scale data analysis, mapping, and modelling. The evaluation indicates that the proposed algorithms are robust and perform well in deriving changes in urban environments. Finally, we explored the implications of urban environment changes in the coupled human-nature system by investigating the responses of building energy use and pollen season to these changes. The resulted products of annual dynamics of urban extents, urban heat island, and phenology indicators from this study offer new datasets for relevant urban studies such as modeling urban sprawl over large areas and investigating ecosystem responses and human activities to urbanization.

ACS Style

Yuyu Zhou; Xuecao Li; Ghassem Asrar; Zhengyuan Zhu; Lin Meng. Satellite-based monitoring urban environmental change and its implications in the coupled human-nature system. 2020, 1 .

AMA Style

Yuyu Zhou, Xuecao Li, Ghassem Asrar, Zhengyuan Zhu, Lin Meng. Satellite-based monitoring urban environmental change and its implications in the coupled human-nature system. . 2020; ():1.

Chicago/Turabian Style

Yuyu Zhou; Xuecao Li; Ghassem Asrar; Zhengyuan Zhu; Lin Meng. 2020. "Satellite-based monitoring urban environmental change and its implications in the coupled human-nature system." , no. : 1.