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(*Corresponding author, #co-first author)

2024

130. Xiao, G.L., Zhang, X.Y., Niu, Q.D., Li, X.G., Li, X.C., Zhong, L.H., Huang, J.X.*. 2024. Winter wheat yield estimation at the field scale using sentinel-2 data and deep learning. Computers and Electronics in Agriculture, 216, 108555. doi: 10.1016/j.compag.2023.108555.  

129. Yin, P.Y., Li, X.C.*, Zhou, Y.Y., Mao, J.F., Fu, Y.S., Cao, W.T., Gong, P., He, W.R., Li, B.G., Huang, J.X., Liu, X.P., Shi, Z.T., Liu, D.L., Guo, J.C. 2024. Urbanization effects on the spatial patterns of spring vegetation phenology depend on the climatic background. Agricultural and Forest Meteorology, 345, 109718. doi: 10.1016/j.agrformet.2023.109718.

 

2023

128. Zhang, Y.X., Li, X.C., Huang, J.X.*, Guan, H.X., Huang, H. 2023. A novel framework for urban land cover change detection with NASA's Black Marble nighttime lights product. IEEE Transactions on Geoscience and Remote Sensing, 61, 1-9. doi: 10.1109/TGRS.2023.3334030.

127. Hao, X.Y., Liu, J.X.*, Heiskanen, J., Maeda, E.E., Gao, S., Li, X.C*. 2023. A robust gap-filling method for predicting missing observations in daily Black Marble nighttime light data. GIScience & Remote Sensing, 60 (1), 2282238. doi: 10.1080/15481603.2023.2282238.

126. Xu, G., Zhu, M.Y. , Chen, B., Salem, M., Xu, Z.B., Li, X.C., Jiao, L.M., Gong, P. 2023. Settlement scaling law reveals population-land tensions in 7000+ African urban agglomerations. Habitat International, 142, 102954. doi: 10.1016/j.habitatint.2023.102954.

125. Yuan, B., Li, X.C.*, Zhou, L., Bai, T.C., Hu, T.Y., Huang, J.X., Liu, D.L., Li, Y.C., Guo, J.C. 2023. Global distinct variations of surface urban heat islands in inter- and intra-cities revealed by local climate zones and seamless daily land surface temperature data. ISPRS Journal of Photogrammetry and Remote Sensing, 204, 1-14. doi: 10.1016/j.isprsjprs.2023.08.012.

124. Geng, M.Q., Li, X.C.*, Mu, H.W., Yu, G.J., Chai, L., Yang, Z.W., Liu, H.M., Huang, J.X., Liu, H., Ju, Z.S. 2023. Human footprints in the Global South accelerate biomass carbon loss in ecologically sensitive regions. Global Change Biology, 29(21), 5881-5895. doi: 10.1111/GCB.16900s.

123. He, W.R., Li, X.C.*, Zhou, Y.Y.*, Shi, Z.T., Yu, G.J., Hu, T.Y., Wang, Y.X., Huang, J.X., Bai, T.C., Sun, Z.C., Liu, X.P., Gong, P. 2023. Global urban fractional changes at a 1km resolution throughout 2100 under eight scenarios of Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs). Earth System Science Data, 15(8), 3623-3639. doi: 10.5194/essd-15-3623-2023.

122. Wang, Y.X., Li, X.C.*, Yin, P.Y., Yu, G.J., Cao, W.T., Liu, J.X., Pei, L., Hu, T.Y., Zhou, Y.Y., Liu, X.P., Huang, J.X., Gong, P. 2023. Characterizing annual dynamics of urban form at the horizontal and vertical dimensions using long-term Landsat time series data. ISPRS Journal of Photogrammetry and Remote Sensing, 203, 199-210. doi: 10.1016/j.isprsjprs.2023.07.025.

121. Ni, H., Yu, L., Li, X.C., Zhao, J.Y., Gong, P.*. 2023. Urban renewal mapping: A case study in Beijing from 2000 to 2020. Journal of Remote Sensing, 3, 0072. doi:10.34133/remotesensing.0072.

120. Dong, Y., Su, W.*, Xuan, F., Li, J.Y., Yin, F., Huang, J.X., Zeng, Y.L., Li, X.C., Tao, W.C. 2023. An effective atmospheric correction method for the wide swath of Chinese GF-1 and GF-6 WFV images on lands. The Egyptian Journal of Remote Sensing and Space Science, 26(3), 732-46. doi: 10.1016/j.ejrs.2023.07.011.

119. Yang, J.L., Dong, J.W. *, Liu, L., Zhao, M.M., Zhang, X.Y., Li, X.C., Dai, J.H., Wang, H.J., Wu, C.Y., You, N.S., Fang, S.B., Pang, Y., He, Y.L., Zhao, G.S., Xiao, X.M., Ge, Q.S*. 2023. A robust and unified land surface phenology algorithm for diverse biomes and growth cycles in China by using harmonized Landsat and Sentinel-2 imagery. 202, 610-636. ISPRS Journal of Photogrammetry and Remote Sensing. doi: 10.1016/j.isprsjprs.2023.07.017.

118. Guan, H.X., Huang, J.X.*, Li, L., Li, X.C., Miao, S.X., Su, W., Ma, Y.Y., Niu, Q.D., Huang, H. 2023. Improved Gaussian mixture model to map the flooded crops of VV and VH polarization data. Remote Sensing of Environment. 295, 113714. doi: 10.1016/j.rse.2023.113714.

117. Su, B.Y., Du, X.P.*, Mu, H.W., Xu, C., Li, X.C., Chen, F., Luo, X.N. 2023. FEPVNet: A Network with Adaptive Strategies for Cross-Scale Mapping of Photovoltaic Panels from Multi-Source Images. Remote Sensing, 15(9), 2469. doi: 10.3390/rs15092469.

116. Dong, Y., Xuan, F., Li, Z.Q., Su, W.*, Guo, H., Huang, X.D., Li, X.C., Huang, J.X. 2023. Modeling the Corn Residue Coverage after Harvesting and before Sowing in Northeast China by Random Forest and Soil Texture Zoning. Remote Sensing, 15(8), 2179. doi:10.3390/rs15082179.

115. Shi, Z.T., Li, X.C.*, Hu, T.Y., Yuan, B., Yin, P.Y., Jiang, D.B. 2023. Modeling the intensity of surface urban heat island based on the impervious surface area. Urban Climate, 49, 101529. doi: 10.1016/j.uclim.2023.101529.

114. Zhuo, W., Huang, H., Gao, X.R., Li, X.C., Huang, J.X.*. 2023. An Improved Approach of Winter Wheat Yield Estimation by Jointly Assimilating Remotely Sensed Leaf Area Index and Soil Moisture into the WOFOST Model. Remote Sensing, 15(7), 1852. doi:10.3390/rs15071825

113. Huang, H., Huang, J.X.*, Wu, Y.T., Zhuo, W., Song, J.J., Li, X.C., Li, L., Su, W., Ma, H., Liang, S.L. 2023. The improved winter wheat yield estimation by assimilating GLASS LAI into a crop growth model with the proposed Bayesian posterior-based ensemble Kalman filter. IEEE Transactions on Geoscience and Remote Sensing. 61, 1-18. doi: 10.1109/TGRS.2023.3259742.

112. Miao, S.X., Zhao, Y.X., Huang, J.X.*, Li, X.C., Wu, R.H., Su, W., Zeng, Y.L., Guan, H.X., Elbasit, A., Mohamed, A.M., Zhang, J.X. 2023. A Comprehensive Evaluation of Flooding's Effect on Crops Using Satellite Time Series Data. Remote Sensing, 15(5), 1305. doi:10.3390/rs15051305.

111. Huang, X.D., Xuan, F., Dong, Y., Su, W.*, Wang, X.S., Huang, J.X., Li, X.C., Zeng, Y.L., Miao, S.X., Li, J. Y. 2023. Identifying Corn Lodging in the Mature Period Using Chinese GF-1 PMS Images. Remote Sensing, 15(4), 894. doi: 10.3390/rs15040894.

110. Xuan, F., Dong, Y., Li, J.Y., Li, X.C., Su, W.*, Huang, X.D., Huang, J.X., Xie, Z.X., Li, Z.Q., Liu, H., Tao, W.C., Wen, Y.N., Zhang, Y. 2023. Mapping crop type in Northeast China during 2013–2021 using automatic sampling and tile-based image classification. International Journal of Applied Earth Observation and Geoinformation, 117, 103718. doi: 10.1016/j.jag.2022.103178. [Highly Cited Paper]

109. He, W.R., Li, X.C.*, Zhou, Y.Y., Liu, X.P., Gong, P., Hu, T.Y., Yin, P.Y., Huang, J.X., Yang, J.Y., Miao, S.X., Wang, X., Wu, T.H. 2023. Modeling gridded urban fractional change using the temporal context information in the urban cellular automata model. Cities, 133, 104146. doi: 10.1016/j.cities.2022.104146.

108. Yin, P.Y., Li, X.C.*, Mao, J.F., Johnson, B.A., Wang, B.Y., Huang, J.X. 2023. A comprehensive analysis of the crop effect on the urban-rural differences in land surface phenology. Science of the Total Environment, 160604. doi: 10.1016/j.scitotenv.2022.160604.

107. Lin, F.Q. *#, Li, X.C. #, Jia, N.Y. #, Feng, F., Huang, H., Huang, J.X., Fan, S.G., Ciais, P., Song, X.P. 2023. The impacts of Russia-Ukraine conflict on global food security. Global Food Security, 36, 100661. doi: 10.1016/j.gfs.2022.100661. [Highly Cited Paper]

 

2022

106. Wang, X. *, Chen, B., Li, X.C., Zhang, Y.X., Ling, X.Y., Wang, J., Li, W.M., Wen, W., Gong, P. 2022. Grid-based Essential Urban Land Use Classification: A Data and Model Driven Mapping Framework in Xiamen City. Remote Sensing, 14(23), 6143. doi: 10.3390/rs14236143.

105. Gómez-Dans, J. L.*, Lewis, P. E., Yin, F., Asare, K., Lamptey, P., Aidoo, K. K. Y., MacCarthy, D. S., Ma, H., Wu, Q., Addi, M., Aboagye-Ntow, S., Doe, C. E., Alhassan, R., Kankam-Boadu, I., Huang, J., Li, X.C. 2022. Location, biophysical and agronomic parameters for croplands in northern Ghana. Earth System Science Data, 14(12), 5387-5420. doi: 10.5194/essd-14-5387-2022.

104.  Zhou, Y.Y. #*, Li, X.C.#, Chen, W., Meng, L., Wu, Q.S., Gong, P., Seto, K.C. 2022. Satellite mapping of urban built-up heights reveals extreme infrastructure gaps and inequalities in the Global South. Proceedings of the National Academy of Sciences of the United States of America, 119(46), e2214813119. doi: 10.1073/pnas.2214813119.

103.  Huang, H., Huang, J.X.*, Feng, Q.L., Liu, J.M., Li, X.C., Wang, X.L., Niu, Q.D. 2022. Developing a dual-stream deep-learning neural network model for improving county-level winter wheat yield estimates in China. Remote Sensing, 14(20), 5280. doi: doi:10.3390/rs14205280.

102.  Guan, H.X., Huang, J.X.*, Li, L., Li, X.C., Ma, Y.Y., Niu, Q.D., & Huang, H. 2022. A novel approach to estimate maize lodging area with PolSAR data. IEEE Transactions on Geoscience and Remote Sensing. doi: 10.1109/TGRS.2022.3216341.

101.  Li, X.G., Geng, H., Zhang, L.Q., Peng, S.W., Xin, Q., Huang, J.X.*, Li, X.C., Liu, S.H., Wang, Y.B.*. 2022. Improving maize yield prediction at the county level from 2002 to 2015 in China using a novel deep learning approach. Computers and Electronics in Agriculture, 202, 107356. doi: 10.1016/j.compag.2022.107356.

100.  Huang, X.D., Huang, J.X.*, Li, X.C., Shen, Q.R., Chen, Z.C. 2022. Early mapping of winter wheat using time series of Sentinel-2 data in Henan province of China. GIScience & Remote Sensing, 59(1), 1534-1549. doi: 10.1080/15481603.2022.2104999.

99.   Yang, C., Liu, H.Z., Li, Q.Q.*, Wang, X.Q., Ma, W., Liu, C.L., Fang, X., Tang, Y.Z., Shi, T.Z., Wang, Q.B., Xu, Y., Zhang, J., Li, X.C., Xu, G., Chen, J.Y., Su, M., Wang, S.Y., Wu, J.J., Huang, L.P., Li, X., Wu, G.F*. 2022. Assessing human efforts on highland developments over Asia in the 21st century. Nature Communications, 13(1), 4955. doi: 10.1038/s41467-022-32648-8.

98.   Guan, H.X., Huang, J.X.*, Li, X.C, Zeng, Y.L., Su, W., Ma, Y.Y., Niu, Q.D., Wang, W. 2022. An improved approach to estimating crop lodging percentage with Sentinel-2 imagery using machine learning. International Journal of Applied Earth Observations and Geoinformation, 113: 102992. doi: 10.1016/j.jag.2022.102992.

97.   Mu, H.W., Li, X.C.*, Zhou, Y.Y., Gong, P., Huang, J.X., Du, X.P., Guo, J.C., Cao, W.T., Sun, Z.C., Xu, C., Liu, D.L.*. Identifying discrepant regions in urban mapping from historical and projected global urban extents. All Earth, 34(1), 167-178. doi: 10.1080/27669645.2022.2104990.

96.   Zhang, Y.X., Huang, J.X.*, Huang, H., Li, X.C., Jin, Y.X., Guo, H., Feng, Q.L., Zhao, Y.Y. 2022. Grassland aboveground biomass estimation through assimilating remote sensing data into a grass simulation model. Remote Sensing, 14(13), 3194. doi:10.3390/rs14133194.

95.   Niu, Q.D., Li, X.C., Huang, J.X.*, Huang, H., Huang, X.D., Su, W., Yuan, W.P. 2022. A 30m annual maize phenology dataset from 1985 to 2020 in China. Earth System Science Data, 14(6), 2851-2864. doi: 10.5194/essd-14-2851-2022.

94.   Wen, Y.N., Li, X.C.*, Mu, H.W., Zhong, L.H., Chen, H., Zeng, Y.L., Miao, S.X., Su, W., Gong, P., Li, B.G., Huang, J.X.*. 2022. Mapping corn dynamics using limited but representative samples with adaptive strategies. ISPRS Journal of Photogrammetry and Remote Sensing, 190, 252-266. doi: 10.1016/j.isprsjprs.2022.06.012.

93.   Tao, W.C., Dong, Y., Su, W.*, Li, J.Y., Xuan, F., Huang, J.X., Yang, J.Y., Li, X.C., Zeng, Y.L., Li, B.G. 2022. Mapping the Corn Residue-Covered Types Using Multi-Scale Feature Fusion and Supervised Learning Method by Chinese GF-2 PMS Image. Frontiers in Plant Science. doi: 10.3389/fpls.2022.901042.

92. Zhuo, W., Hunag, J.X., Xiao, X.M., Huang, H., Bajgain, R., Wu, X.C., Gao, X.R., Wang, J., Bai, T.C., Li, X.C., Wagle, P. 2022. Assimilating remote sensing-based VPM GPP into the WOFOST model for improving regional winter wheat yield estimation. European Journal of Agronomy, 139, 126556. doi: 10.1016/j.eja.2022.126556.

91. Li, L.Z., Li, X.C.*, Asrar, G., Zhou, Y.Y., Chen, M., Zeng, Y.L., Li, X.J., Li, F., Luo, M., Sapkota, A., Hao, D.L.*. 2022. Detection and attribution of long-term and fine-scale changes in spring phenology over urban areas: A case study in New York State. International Journal of Applied Earth Observations and Geoinformation, 110, 102815. doi: 10.1016/j.jag.2022.102815.

90. Li, X.C., Zhou, Y.Y.*, Gong, P. 2022. Diversity in global urban sprawl patterns revealed by Zipfian dynamics. Remote Sensing Letters, 1-11. doi: 10.1080/2150704X.2022.2073794.

89. Huang, H., Huang, J.X.*, Li, X.C., Zhuo, W., Wu, Y.T., Niu, Q.D., Su, W., Yuan, W.P. 2022. A dataset of winter wheat aboveground biomass in China during 2007-2015 based on data assimilation. Scientific Data, 9(1), 200. doi: 10.1038/s41597-022-01305-6.

88. Yu, G.J., Xie, Z.X., Li, X.C.*, Wang, Y.X., Huang, J.X., Yao, X.C. 2022. The Potential of 3-D Building Height Data to Characterize Socioeconomic Activities: A Case Study from 38 Cities in China. Remote Sensing, 14, 2087. doi: 10.3390/rs14092087.

87. Mu, H.W., Li, X.C.*, Wen, Y.N., Huang, J.X., Du, P.J., Su, W., Miao, S.X., Geng, M.Q. 2022. A global record of annual terrestrial Human Footprint dataset from 2000 to 2018. Scientific Data, 9, 176. doi: 10.1038/s41597-022-01284-8. [Highly Cited Paper]

86.   Cao, W.T., Zhou, Y.Y.*, Guneralp, B., Li, X.C., Zhao, K.G., Zhang, H.G. 2022. Increasing global urban exposure to flooding: An analysis of long-term annual dynamics. Science of the Total Environment, 817, 153012. doi: 10.1016/j.scitotenv.2022.153012.

85.   Zhao, M., Cheng, C.X.*, Zhou, Y.Y.*, Li, X.C., Shi, S., Song, C.Q. 2022. A global dataset of annual urban extents (1992-2020) from harmonized nighttime lights. Earth System Science Data, 141(2), 517-534. doi: 10.5194/essd-14-517-2022. [Highly Cited Paper]

84.   Zhang, J., Li, X.C., Zhang, C.C., Yu, L., Wang, J.Z., Wu, X.Y., Hu, Z.W., Zhai, Z.H., Li, Q.Q., Wu, G.F., Shi, T.Z.*. 2022. Assessing spatialtemporal variations and predicting changes in ecosystem service values in the Guangdong-HongKong-Macau Greater Bay Area. GIScience and Remote Sensing, 1-16. doi: 10.1080/15481603.2021.2022427.

83.  Yang, C., Guo, W.H., Zhang, C.C., Cui, A.H., Li, X.C., Zhao, T.H., Liu, H.Z., Shi, T.Z., Xu, G., Fang, X., Liu, X., Zhang, K.Y., Gong, P., Li, Q.Q.*, Wu, G.F.* 2022. Characteristics and trends of hillside urbanization in China from 2007 to 2017. Habitat International, 120, 102502. doi: 10.1016/j.habitatint.2021.102502.

82.  Mu, H.W., Li, X.C.*, Ma, H.J., Du, X.P., Huang, J.X., Su, W., Zhen, Y., Xu, C., Liu, H.L., Yin, D.Q, Li, B.G. 2022. Evaluation of the policy-driven ecological network in the Three-North Shelterbelt region of China. Landscape and Urban Planning, 218, 104305. doi: 10.1016/j.landurbplan.2021.104305. [Highly Cited Paper]

81.   Li, L.Z., Hao, D.L., Li, X.C., Chen, M., Zhou, Y.Y., Jurgens, D., Asrar, G., Sapkota, A*. 2022. Satellite-based phenology products and in-situ pollen dynamics: A comparative assessment. Environmental Research, 204, 111937. doi: 10.1016/j.envres.2021.111937.

80.   Zhang, Y.H., Yin, P.Y., Li, X.C.*, Niu, Q.D., Wang, Y.X., Cao, W.T., Huang, J.X., Chen, H., Yao, X.C., Yu, L., Li, B.G.. 2022. The divergent response of vegetation phenology to urbanization: A case study of Beijing city, China. Science of the Total Environment, 803, 150079. doi: 10.1016/j.scitotenv.2021.150079.

2021

79. Cao, B.W., Yu, Le.*, Li, X.C., Chen, M., Li, X., Hao, P., & Gong, P. 2021. A 1 km global cropland dataset from 10000 BCE to 2100 CE. Earth System Science Data, 13(11), 5403-5461. doi: 10.5194/essd-13-5403-2021.

78.   Wang, Y., Zhang, G.J.*, Gong, P.*, Dickinson, R.E, Fu, R., Li, X.C., Yang, J., Liu, S., He, Y.J., Li, L.J., Wang, B., Xu, B. 2021. Winter Warming in North America Induced by Urbanization in China. Geophysical Research Letters, 48, e2021GL095465. doi: 10.1029/2021GL095465.

77.   Li, X.C., Zhou, Y.Y.*, Hejazi, M., Wise, M., Vernon, C., Iyer, G., Chen, W.  2021. Global urban growth between 1870 and 2100 from integrated high resolution mapped data and urban dynamic modelling. Communication Earth & Environment, 2(1), 201. doi: 10.1038/s43247-021-00273-w.

76.   Dong, Y., Yin, D.Q.*, Li, X., Huang, J.X., Su, W., Li, X.C., Wang, H.S. 2021. Spatial-temporal evolution of vegetation NDVI in association with climatic, environmental and anthropogenic factors in the Loess Plateau, China during 2000-2015: Quantitative analysis based on geographical detector model. Remote Sensing, 13(21), 4380. doi: 10.3390/rs13214380.

75.   Zhao, Q., Yu, L.*, Li, X.C., Peng, D.L., Zhang, Y.G., Gong, P.  2021. Progress and Trends in the Application of Google Earth and Google Earth Engine. Remote Sensing,  13(18), 3778. doi: 10.3390/rs13183778.

74.   Johnson, B.A.*., Ronald, C.E., Li, X.C., Pankaj, K., Dasgupta, R., Avtar, R., Magcale-Macandog, D.B. 2021. High-resolution urban change modeling and flood exposure estimation at a national scale using open geospatial data: A case study of the Philippines. Computer, Environment and Urban System, 90, 101704. doi: 10.1016/j.compenvurbsys.2021.101704.

73.   Tao, W.C., Xie, Z.X., Zhang, Y., Li, J.Y., Xuan, F., Li, X.C., Huang, J.X., Su, W.*, Yin, D.Q. 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(15), 2903. doi: 10.3390/rs13152903.

72.   Meng, L., Mao, J.F.*, Ricciuto, D.M., Shi, X.Y., Richardson, A.D., Hanson, P.J., Warren, J.M., Zhou, Y.Y., Li, X.C., Zhang, L., Schadel, C. 2021. Evaluation and modification of ELM seasonal deciduous phenology against observations in a southern boreal peatland forest. Agricultural and Forest Meteorology, 308, 108556. doi: 10.1016/j.agrformet.2021.108556.

71.   Chen, B.*, Song, Y.M., Tu, Y., Theobald, D., Zhang, T., Ren, Z.H., Li, X.C.Yang, J., Wang, J., Wang, X., Gong, P., Bai, Y.Q.*, & Xu, B.* 2021. Mapping essential urban land use categories with open big data: results for five metropolitan areas in the United States of America. ISPRS Photogrammetry & Remote Sensing. doi: 10.1016/j.isprsjprs.2021.06.010.

70.   Li, X.C.*, Zhang, J., Li, Z.Y., Hu, T.Y., Wu, Q.S., Zhao, Y.Y., Yang, J., Huang, J.X., Zhou, Y.Y., Liu, X.P., Gong, P, & Wang, X. 2021. The critical role of temporal contexts in evaluating urban cellular automata models. GIScience & Remote Sensing. doi: 10.1080/15481603.2021.1946261.

69.   Ni, H., Gong, P.*, & Li, X.C. 2021. Extraction of old towns in Hangzhou (2000-2018) from Landsat time series stacks. Remote Sensing, 13, 2438. doi: 10.3390/rs13132438.

68.   Zhao, X., Chen, W., Zhou, Y.Y.*, Li, X.*, Li, X.C., & Li, D.R. 2021. Mapping hourly population dynamics using remotely sensed and geo-spatial data: a case study in Beijing, China. GIScience & Remote Sensingdoi: 10.1080/15481603.2021.1935128.

67.   Mu, H.W., Li, X.C.*, Du, X.P., Huang, J.X., Su, W., Hu, T.Y., Wen, Y.N., Yin, P.Y., Han, Y., & Xue, F. 2021. Evaluation of light pollution in global protected areas from 1992 to 2018. Remote Sensing, 13(9): 1849. doi: 10.3390/rs13091849.

66.   Cao, W.T., Zhou, Y.Y.*, Li, R.*, Li, X.C., & Zhang, H.G. 2021. Long-term annual urban sprawl (1986-2017) in China’s largest archipelago. Science of the Total Environment. doi: 10.1016/j.scitotenv.2021.146015.

65.   Li, X.T., Hu, T.Y., Gong, P.*, Du, S.H., Chen, B., Li, X.C., & Dai, Q. 2021. Mapping essential urban land use categories in Beijing with a fast area of interest (AOI) based method. Remote Sensing, 13(3), 477. doi: 10.3390/rs13030477.

64.   Zhang, Z.X.*, Kass, J.M., Mammola, S., Koizumi, I., Li, X.C., Tanaka, K., Ikeda, K, Suzuki, T., Yokota, M., & Usio, N. 2021. Lineage-level distribution models lead to more realistic climate change predictions for a threatened crayfish. Diversity and Distributions, 1-12. doi: 10.1111/ddi.13225.

63.   Shi, C., Qu, L.Q., Zhang, W.Q.*, & Li, X.C. 2021. A systematic review on sloping farmland use based on a perspective of bibliometric analysis. Agricultural Water Management, 244, 106564. doi: 10.1016/j.agwat.2020.106564.

2020

62.   Tian, H., Pei, J., Huang, J., Li, X.C., Wang, J., Zhou, B., Qin, Y.*, & Wang, L. 2020. Garlic and Winter Wheat Identification Based on Active and Passive Satellite Imagery and the Google Earth Engine in Northern China, Remote Sensing, 12, 3539. doi: 10.3390/rs12213539.

61.   Zhang, J., Yu, L., Li, X.C., Zhang, C.C., Shi, T.Z., Wu, X.Y., Yang, C., Gao, W.X., Li, Q.Q.*, & Wu, G.F. 2020. Exploring annual urban expansions in Guangdong-Hong Kong-Macau Greater Bay Area: Spatiotemporal features and driving factors in 1986-2017. Remote Sensing, 12(16)2516.  doi:10.3390/rs12162615.

60.   Hackman, K.O.*, Li, X.C., Gyambibi, D.A., Asamoach, E.A., & Nelson, I.D. 2020. Analysis of geo-spatiotemporal data using machine algorithms and reliability enhancement for urbanization decision support. International Journal of Digital Earth. 1-16. doi: 10.1080/17538947.2020.1805036.

59.   Weber, M., Hao, D.L., Asrar, G.R., Zhou, Y., Li, X.C., & Chen, M*. 2020. Exploring the use of DSCOVR/EPIC satellite observations to monitor vegetation phenology. Remote Sensing, 12, 2384. doi: 10.3390/rs12152384.

58.   Zhao, J.Y., Yu, L.*, Xu, Y.D., Li, X.C., Liu, H., Huang, X.M., Wang, D., Ren, C., & Gong, P. 2020. Exploring differences in land surface temperature between the city centers and urban expansion areas of China’s major cities. International Journal of Remote Sensing, 41, 8963-8983. doi: 10.1080/01431161.2020.1797216.

57.   Zhao, M., Zhou, Y.Y.*, Li, X.C.Cheng, W.M.*, Zhou, C.H., Ma, T., Li, M.C., & Huang, K.  2020. Mapping urban dynamics (1992-2018) in Southeast Asia using fused nighttime light data from DMSP and VIIRS. Remote Sensing of Environment. doi: 10.1016/j.res.2020.111980.

56.   Hu, T.Y., Li, X.C.Gong, P.*, Yu, W.C., & Huang, X.C. 2020. Evaluating the effect of plain afforestation project and future spatial suitability in Beijing. China Science: Earth Science, 63. doi: 10.1007/s11430-019-9636-0.

55.   Sapkota, A.*, Dong, Y., Li, L.Z., Asrar, G., Zhou, Y.Y., Li, X.C., Coates, F., Spanier, A.J., Matz, J., Bielory, L., Breitenother, A.G., Mitchell, C., &  Jiang, C.S. 2020. Association Between Changes in Timing of Spring Onset and Asthma Hospitalization in Maryland. JAMA Network Open3, e207551. doi: 10.1001/jamanetworkopen.2020.7551.

54.   Li, X.C., Gong, P.*, Zhou, Y.Y.*, Wang, J., Bai, Y.Q., Chen, B., Hu, T.Y., Xiao, Y.X., Xu, B., Yang, J., Liu, X.P., Cai, W.J., Huang, H.B., Wu, T.H., Wang, X., Lin, P.,  Li, X., Chen, J., He, C.Y., Li, X., Yu, L., Clinton, N., & Zhu, Z.L. 2020. Mapping global urban boundaries from the global artificial impervious area (GAIA) data. Environmental Research Letters, 15, 094044. doi: 10.1088/1748-9326/ab9be3. [Highly Cited Paper]

53.   Li, X.C., Zhou, Y.Y.*, Zhao, M., & Zhao, X. 2020. A harmonized global nighttime light dataset 1992-2018. Scientific Data, 7, 168. doi: 10.1038/s41597-020-0510-y.  [Highly Cited Paper]

52.   Li, X.C., Zhou, Y.Y.*, & Chen, W. 2020. An improved urban cellular automata model by using the trend adjusted neighborhood. Ecological Processes, 9, 28. doi: 10.1186/s13717-020-00234-9.

51.   Liu, X.P., Huang, Y.H., Xu, X.C., Li, X.C., Li, X.*, Ciasi, P., Gong, K., Ziegler, A.D., Chen, A.P., Gong, P., Chen, J., Hu, G.H., Chen, Y.M., Wang, S.J., Wu, Q.S., Huang, K.N., Estes, L., & Zeng, Z.Z.* 2020. High-spatiotemporal-resolution mapping of global urban change from 1985 to 2015. Nature Sustainability, doi: 10.1038/s41893-020-0521-x. [Highly Cited Paper]

50.   Liu, X., Zhou, Y.Y.*, Yue, W.Z.*, Li, X.C., Liu, Y., & Lu, D.B. 2020. Spatiotemporal patterns of summer urban heat island in Beijing, China using an improved land surface temperature. Journal of Cleaner Production257, 120529. doi: 10.1016/j.jclepro.2020.120529.

49.   Li, X.C., Zhou, Y.Y.*, Gong, P., Seto, K.C., & Clinton, N. 2020. Developing a method to estimate building height from Sentinel-1 data. Remote Sensing of Environment, 240, 111705. doi: 10.1016/j.res.2020.111705.

48.   Li, X.C., Zhou, Y.Y.*, Zhu, Z.Y., & Cao, W.T. 2020. A national dataset of 30-m annual urban extent dynamics (1985–2015) in the conterminous United States. Earth System Science Data12, 357-371. doi: https://doi.org/10.5194/essd-12-357-2020.

47.   Meng, L., Mao, J.M.*, Zhou, Y.Y.*, Richardson, A.D., Lee, X.H., Thornton, P.E., Ricciuto, D.M., Li, X.C., Dai, Y.J., Shi, X.Y., & Jia, G.S. 2020. Urban warming advances spring phenology but reduces temperature response of plants in the conterminous United States. 2020. Proceedings of the National Academy of Sciences of the United States of America, 117, 4228-4233doi:10.1073/pnas.1911117117.

46.   Cao, W.T., Zhou, Y.Y.*, Li, R.*, & Li, X.C. 2020. Mapping changes in coastlines and tidal flats in developing islands using the full time series of Landsat images. Remote Sensing of Environment, 239, 1-11. doi: 10.1016/j.res.2020.111665.

45.   Meng, L., Zhou, Y.Y.*, Li, X.C., Asrar, G.R., Mao, J.F., Wanamaker, A.D., & Wang, Y.Q. 2020. Divergent responses of spring phenology to daytime and nighttime warming. Agricultural and Forest Meteorology, 281, 107832. doi: 10.1016/j.agrformet.2019.107832.

44.   Gong, P.*, Li, X.C., Wang, J.*, Bai, Y., Chen, B., Hu, T.Y., Liu, X.P., Xu, B., Yang, J., Zhang, W., & Zhou, Y.Y. 2020. Annual maps of global artificial impervious areas (GAIA) between 1985 and 2018. Remote Sensing of Environment, 236, 111510. doi: 10.1016/j.rse.2019.111510. [Highly Cited Paper] [Hot Paper].

43.   Gong, P.*, Chen, B., Li, X.C., Liu, H., Wang, J., Bai, Y.Q., Chen, J.M., Chen, X., Feng, S.L., Huang, H.B., Huang, X.C., Jie, Y.W., Kang, Y.D., Lei, G.B., Li, A.N, Li, X.T., Li, X., Li, Y.C., Li, Z.L., Li, Z.D., Liu, C., Liu, C.X., Liu, M.C., Liu, S.G., Mao, W.L., Miao., C.H., Ni, H., Suen, H.P., Sun, B., Sun, F.D., Sun, J., Sun, L., Tian. T., Tong, X.H., Tseng, Y.S., Tu, Y., Wang, H., Wang, L., Wang, X., Wang, Z.M., Wu, T.H., Yang, J., Yue, W.Z., Zeng, H.D., Zhang, K., Zhang, N., Zhang, T., Zhang, Y., Zhao, F., Zheng, Y.C., Zhou, Q.M., Clinton, N., Zhu, Z.L., & Xu, B*. 2020. Mapping essential urban land use categories in China (EULUC-China): Preliminary results for 2018. Science Bulletin, 65, 182-187. doi: 10.1016/j-sclb.2019.12.007. [Highly Cited Paper].

2019

42.   Wei, J.Z., Zheng, K., Zhang, F., Fang, C., Zhou, Y.Y., Li, X.C., Li, F.M.*, & Ye, J.S*. 2019. Migration of rural residents to urban areas drives grassland vegetation increase in China's Loess Plateau. Sustainability, 11(23), 6764doi:10.3390/su11236764.

41.   Zhao, M., Zhou, Y.Y.*, Li, X.C., Zhou, C.H., Cheng, W.M.*, & Li, M.C. 2019. Building a series of consistent night-time light data (1992-2018) in southeast Asia by integrating DMSP-OLS and NPP VIIRS. IEEE Transactions on Geoscience and Remote Sensing, 1-14doi: 10.1109/TGRS.2019.2949797. [Highly Cited Paper].

40.   Zhao, M., Zhou, Y.Y.*, Li, X.C., Cao, W.T., He, C.Y., Yu, B.L., Li, X., Elvidge, C., Cheng, W.M., & Zhou, C.H. 2019. Applications of satellite remote sensing  of nighttime light observations: advances, challenges, and perspectives. Remote Sensing, 11(17), 1971doi: 10.3390/rs11171971. [Highly Cited Paper].

39.   Li, X.C., Zhou, Y.Y.*, Meng, L., Asrar, G., Lu, C.Q., & Wu, Q.S. 2019. A dataset of 30-meter annual vegetation phenology indicators (1985-2015) in urban areas of the conterminous Unites States. Earth System Science Data11(2), 881-894. doi:10.5194/essd-11-881-2019.

38.   Ma, M.H., Xue, F.*, Dang, A.R., Li, X.C., & Hu, T.Y. 2019. Study on the spatial-temporal change of vegetation coverage between the belts of Beijing's  main urban area based on dynamic remote sensing data. Journal of Environmental Engineering Technology (Chinese), 9(4), 404-413. doi: 10.12153/j.issn.1674-991X.2019.05.141.

37.   Hu, T.Y.*, Huang, X.C., Li, X.C., Liang, L., & Xue, F. 2019. Toward a Better Understanding of Urban Sprawl: Linking Spatial Metrics and Landscape Networks Dynamics. In: Geertman S., Zhan Q., Allan A., Pettit C. (eds) Computational Urban Planning and Management for Smart Cities. CUPUM 2019. Lecture Notes in Geoinformation and Cartography. Springer, Cham. doi: 10.1007/978-3-030-19424-6_10.

36.   Wu, Q.S.*, Lane, C.R., Li, X.C., Zhao, K.G., Zhou, Y.Y., Clinton, N., DeVries, B., Golden, H.E., & Lang, M.W. 2019. Integrating LiDAR data and multi-temporal aerial imagery to map wetland inundation dynamics using Google Earth Engine. Remote Sensing of Environment228, 1-13. doi: 10.1016/j.rse.2019.04.015.

35.   Li, X.C., Zhou, Y.Y.*, Eom, J.Y., Yu, S., & Asrar, G.R. 2019. Projecting global urban area growth through 2100 based on historical time-series data and future Shared Socioeconomic Pathways. Earth’s Future7(4), 351-362doi:10.1029/2019EF001152.

34.   Gong, P.*, Li, X.C., & Zhang, W. 2019. 40-year (1978-2017) human settlement changes in China reflected by impervious surfaces from satellite remote sensing. Science Bulletin64, 756-763. doi: 10.1016/j.scib.2019.04.024. [Highly Cited Paper][Hot Paper].

33.   Li, X.C., Zhou, Y.Y.*, Meng, L., Asrar, G.R., Sapkota, A., & Coates, F. 2019. Characterizing the relationship between satellite phenology and pollen season: a case study of birch. Remote Sensing of Environment, 222,269-274. doi: 10.1016/j.rse.2018.12.036.

2018

32.   Zhou, Y.Y.*, Li, X.C., Asrar, G.R., Smith, S.J., & Imhoff, M. 2018. A global record of annual urban dynamics (1992-2013) from nighttime lights.  Remote Sensing of Environment, 219, 206-220. doi: 10.1016/j.rse.2018.10.015. [Highly Cited Paper].

31.   Yu, L.*, Xu, Y.D., Xue, Y.M., Li, X.C., Cheng, Y.Q., Liu, X.X., Porwal, A., Holden, E.J., Yang, Y., & Gong, P. 2018. Monitoring surface mining belts using multiple remote sensing datasets: A global perspective. Ore Geology Reviews, 101, 675-687doi: 10.1016/j.oregeorev.2018.08.019.

30.   Li, X.C., Zhou, Y.Y.*, Zhu, Z.Y., Liang, L., Yu, B.L, & Cao, W.T. 2018. Mapping annual urban dynamics (1985-2015) using time series of Landsat data.  Remote Sensing of Environment, 216, 674-683. doi: 10.1016/j.rse.2018.07.030.

29.   Lyu, H.B., Lu, H.*, Mou, L.C., Li, W.Y., Wright, J., Li, X.C., Li, X.L., Zhu, X.X., Wang, J., Yu, L., & Gong, P. 2018. Long-Term Annual Mapping of Four Cities on Different Continents by Applying a Deep Information Learning Method to Landsat Data. Remote Sensing, 10(3), 471. doi: 10.3390/rs10030471.

2017

28.   Li, X.C., Lu, H.*, Zhou, Y.Y., Hu, T.Y., Liang, L., Liu, X.P., Hu, G.H., & Yu, L. 2017. Exploring the performance of spatio-temporal assimilation in an urban cellular automata model. International Journal of Geographic Information Science., 31(11), 2195-2215doi: 10.1080/13658816.2017.1357821.

27.   Li, X.C., Gong, P.*, Yu, L., & Hu, T.Y. 2017. A segment derived patch-based logistic cellular automata for urban growth modeling with heuristic rules. Computers, Environment & Urban Systems65, 140-149doi: 10.1016/j.compenvurbsys.2017.06.001.

26.   Li, X.C., Zhou, Y.Y.*, Asrar, G.R., & Meng, L. 2017. Characterizing spatiotemporal dynamics in phenology of urban ecosystems based on Landsat  data. Science of the Total Environment. 605, 721-734. doi: 10.1016/j.scitotenv.2017.06.245.

25.   Li, X.M., Zhou, Y.Y.*, Asrar, G.R., Imhoff, M, & Li, X.C. 2017. The surface urban heat island response to urban expansion: A panel analysis for the conterminous United States. Science of the Total Environment605, 426-435. doi: 10.1016/j.scitotenv.2017.06.229.

24.   Li, X.C., & Zhou, Y.Y.* 2017. A Stepwise Calibration of Global DMSP/OLS Stable Nighttime Light Data (1992–2013). Remote Sensing9(6), 637doi:10.3390/rs9060637.

23.   Liang, L.*, Li, X.C., Huang, Y.B., Qin, Y.C., & Huang, H.B. 2017. Integrating remote sensing, GIS and dynamic models for landscape-level simulation of forest insect disturbance. Ecological Modeling354, 1-10doi: 10.1016/j.ecolmodel.2017.03.007.

22.   Li, C.C., Gong, P.*, Wang, J., Zhu, Z.L., Biging, G.S., Yuan, C., Hu, T.Y., Zhang, H.Y., Wang, Q., Li, X.C., Liu, X.X., Xu, Y.D., Guo, J., Liu, C.X., Heckman, K., Zhang, M.N., Cheng, Y.Q., Yu, L., & Yang, J. 2017. The first all-season sample set for mapping global land cover with Landsat-8 data. Science Bulletin, 62(7), 508-515doi: 10.1016/j.scib.2017.03.011.

21.   Reynolds, R., Liang, L.*, Li, X.C., & Dennis, J. 2017. Monitoring Annual Urban Changes in a Rapidly Growing Portion of Northwest Arkansas with a 20-Year Landsat Record. Remote Sensing, 9(1), 71. doi:10.3390/rs9010071.

20.   Li, X.C., & Zhou, Y.Y.* 2017. Urban mapping using DMSP/OLS stable night-time light: a review. International Journal of Remote Sensing38(21), 6030-6046doi:10.1080/01431161.2016.1274451. [Highly Cited Paper].

19.   Yu, L.*, Li, X.C., Li, C.C., Zhao, Y.Y., Niu, Z.G., Huang, H.B., Wang, J., Cheng, Y.Q., Si, Y.L., Yu, C.Q., Fu, H.H., & Gong, P. 2017. Using a global reference sample set and a cropland map for area estimation in China. Science China: Earth Science60, 277-285doi:10.1007/s11430-016-0064-5.

18.   Li, X.C., Zhou, Y.Y.*, Asrar, G., Mao, J.F., Li, X.M., & Li, W.Y., 2017. Response of vegetation phenology to urbanization in the conterminous United States. Global Change Biology, 23(7), 2818-2830doi:10.1111/gcb.13562.

2016

17.   Zhong, L.H., Yu, L.*, Li, X.C., Hu, L.N., & Gong, P. 2016. Rapid corn and soybean mapping in US Corn Belt and neighboring areas. Scientific Reports, 6, 36240. doi:10.1038/srep36240.

16.   Li, X.C. & Gong, P.* 2016. An “exclusion-inclusion” framework for extracting human settlements in rapidly developing regions of China from Landsat images. Remote Sensing of Environment, 188, 286-296doi:10.1016/j.rse.2016.08.029.

15.   Li, X.C., Yu L*., Xu, Y.D., Yang, J., & Gong. P. 2016. Ten years after Hurricane Katrina: monitoring recovery in New Orleans and the surrounding areas using remote sensing. Science Bulletin611460-1470doi:10.1007/s11434-016-1167-y.

14.   Li, X.C.Le, Y.*, Sohl, T., Clinton, N., Li, W.Y., Zhu, Z.L., Liu, X.P., & Gong, P. 2016. A cellular automata downscaling based 1 km global land use datasets (2010–2100). Science Bulletin, 61, 1651-1661. doi:10.1007/s11434-016-1148-1.

13.   Li, X.C. & Gong, P.* 2016. Urban growth models: progress and perspective. Science Bulletin61, 1637-1650. doi:10.1007/s11434-016-1111-1.

12.   Liang, L.*, Hawbaker, T.J., Zhu, Z.L., Li, X.C., & Gong, P. 2016. Forest disturbance interactions and successional pathways in the Southern Rocky Mountains. Forest Ecology and Management375, 35-45. doi: 10.1016/j.foreco.2016.05.010.

11.   Gong, P.*, Yu, L., Li, C.C., Wang, J., Lu, Liang., Li, X.C., Ji, L.Y., Bai, Y.Q., Cheng, Y.Q., & Zhu, Z.L. 2016. A new research paradigm for global land cover mapping. Annals of GIS, 22(2), 87-102doi: 10.1080/19475683.2016.1164247.

10.   Hu, T.Y., Yang, J.*, Li, X.C., & Gong, P. 2016. Mapping urban land use by using Landsat images and open social data. Remote Sensing, 8(2), 151. doi:10.3390/rs8020151. [Highly Cited Paper].

9.     Chen, H., Yu, C.*, Li, C.S., Xin, Q.C., Huang, X., Zhang, J., Yue, Y.L., Huang, G.R, Li, X.C. & Wang, W. 2016. Modeling the impacts of water and fertilizer management on the ecosystem service of rice rotated cropping systems in China. Agriculture, Ecosystems and Environment, 219, 49-57. doi: 10.1016/j.agee.2015.11.023.

2015

8.     Li, X.C., Gong, P.* & Lu, Liang. 2015. A 30-year (1984–2013) record of annual urban dynamics of Beijing City derived from Landsat data. Remote Sensing of Environment, 16678-90doi: 10.1016/j.rse.2015.06.007. [Highly Cited Paper].

7.     Li, X.C., Liu, X.P.* & Gong, P. 2015. Integrating ensemble-urban cellular automata model with an uncertainty map to improve the performance of a single model. International Journal of Geographical Information Science, 29762-785doi: 10.1080/13658816.2014.997237.

2014

6.     Yu, L., Wang, J, Zhao, Y.Y., Cheng, Q., Hu, L. Y., Liu, S., Yu, L., Wang, X.Y., Zhu, P., Li, X.Y., Xu, Y., Li, C.C., Fu, W., Li, X.C., Li, W.Y., Liu, C.X., Cong, N., Zhang, H., Sun, F.D., Bi, X.F., Xin, Q.C., Li, D.D., Yan, D.H., Zhu, Z.L., Goodchild, M., & Gong. P.* 2014. Meta-discoveries from a synthesis of satellite-based land-cover mapping research. International Journal of Remote Sensing35(13), 4573-4588doi: 10.1080/01431161.2014.930206.

5.     Li, X.C., Liu, X.P.* & Yu, L. 2014. A systematic sensitivity analysis of constrained cellular automata model for urban growth simulation based on different transition rules. International Journal of Geographical Information Science, 28(7)1317-1335doi: 10.1080/13658816.2014.883079.

4.     Li, X.C., Liu, X.P.* & Yu, L. 2014. Aggregative model-based classifier ensemble for improving land-use/cover classification of Landsat TM Images.  International Journal of Remote Sensing, 35(4)1481-1495doi: 10.1080/01431161.2013.878061.

3.     Yu, L., Wang, J., Li, X.C., Li, C.C., Zhao, Y.Y., & Gong, P.* 2014. A multi-resolution global land cover dataset through multisource data aggregation.  Science China: Earth Science, 57(10), 2317-2329. doi:10.1007/s11430-014-4919-z.

2.     Yu, C.*, Li, C.S., Xin, Q.C., Chen, H., Zhang, J., Zhang, F., Li, X.C., Clinton, N., Huang, X., Yue, Y.L & Gong, P. 2014. Dynamic assessment of the impact of drought on agricultural yield and scale-dependent return periods over large geographic regions. Environmental Modeling & Software, 62, 454-464doi: 10.1016/j.envsoft.2014.08.004.

2011

1.     Dong, X.Z., Li, A.M., Li, X.C., & Li, B.X. 2011. The study of land use and its potential based on GIS. Surveying and Mapping (Chinese)34, 271-273. doi: 10.3969/j.issn.1674-5019.2011.06.009.