[1] LI Guoqing, BAI Yongqing, YANG Xuan et al. Automatic land cover classification method for high resolution remote sensing images based on Deep Learning. Journal of Earth Information Science, 23, 1690-1704(2021).

[2] JACH L, WARRACH‐SAGI K, INGWERSEN J et al. Land cover impacts on land‐atmosphere coupling strength in climate simulations with WRF over Europe. Journal of Geophysical Rese-arch： Atmospheres, 125(2020).

[3] LI Ying, ZHAO Wenji, LI Xiaolin. Application of Classification and Recognition Technology of Remote Sensing Images in Land Resource Survey. Journal of Changchun University of Science and Technology, 261-264(2001).

[4] LUNETTA R S, KNIGHT J F, EDIRIWICKREMA J et al. Land-cover change detection using multi-temporal MODIS NDVI data. Remote Sensing of Environment, 105, 142-154(2006).

[5] STEVENS F R, GAUGHAN A E, NIEVES J J et al. Comparisons of two global built area land cover datasets in methods to disaggregate human population in eleven countries from the global South. International Journal of Digital Earth, 13, 78-100(2020).

[6] LEI Yanhua, DING Guodong, LI Zimeng et al. Land use/cover change and ecosystem service value response in the Beijing Tianjin sandstorm source control project area. Chinese Desert, 41, 29-40(2021).

[7] LI Hongying. Research on land cover classification in Zhejiang Province based on MODIS data(2011).

[8] LING Z, GUANGSHUAI J, DEJUN G. Integrating land-cover products based on ontologies and local accuracy. Information, 12, 236-236(2021).

[9] LIU Jiyuan, LIU Mingliang, ZHUANG Dafang et al. Spatial pattern analysis of recent land use changes in China. Chinese Science （Series：Earth Science）, 1058-1060(2002).

[10] MELAAS E K, FRIEDL M A, ZHU Z. Detecting interannual variation in deciduous broadleaf forest phenology using Landsat TM/ETM+ data. Remote Sensing of Environment, 132, 176-185(2013).

[12] HAGENSIEKER R, WASKE B. Evaluation of multi-frequency SAR images for tropical land cover mapping. Remote Sensing, 10, 257-257(2018).

[13] WANG Chunyan, WANG Liuming, ZHANG Yuan et al. Application of SEaTH based decision tree method in regional scale land cover classification. Journal of Lanzhou University （Natural Science Edition）, 55, 11-18(2019).

[14] LIU Jiyuan, ZHUANG Dafang, LING Yangrong. Research on Comprehensive Classification of Vegetation in Northeast China Based on GIS. Journal of Remote Sensing, 12, 285-291(1998).

[15] LI Shaoying, LIU Xiaoping, LI Xia et al. Research progress on simulation models and applications of land use change. Journal of Remote Sensing, 21, 329-340(2017).

[16] LIAO Shunbao, JIANG Xiao. Exploration of methods for improving the classification accuracy of land cover products based on Terrain Parameters. Journal of Fujian Normal University （Natural Science Edition）, 35, 7-18(2019).

[17] LIAO Shunbao, YUE Yanlin. Method for improving the accuracy of land cover classification based on time-series NDVI map library. Journal of Agricultural Engineering, 34, 241-248(2018).

[18] GONG Hui, WANG Han, LIANG Wan et al. Factor analysis of haze formation in the Beijing Tianjin Hebei region. China's Environmental Protection Industry, 34-39(2020).

[19] LIU Jiyuan, KUANG Wenhui, ZHANG Zengxiang et al. The basic characteristics and spatial pattern of land use change in China since the late 1980s. Journal of Geography, 69, 3-14(2014).

[20] LIU Tianen, WANG Lidong. Important progress and difficulties in industrial transfer in the Beijing Tianjin Hebei region. Regional Economic Review, 117-123(2022).

[21] LUO Chenfeng. Remote Sensing Research on Land Cover Classification and Change Monitoring in China(2005).

[22] MU Aiying, LU Yan. The "Dual Carbon" goal leads the collaborative development of the Beijing Tianjin Hebei Industrial Chain Cluster - Overview of the "2021 Beijing Tianjin Hebei Collaborative Development Forum under the vision of Carbon Neutrality. Journal of Hebei University of Economics and Business （Comprehensive Edition）, 21, 90-95(2021).

[23] SUI Xiuzhi, YAN Zhigang, KANG Dongwei et al. Analysis of inter city differences in the ecological security pattern of Beijing Tianjin Hebei. Journal of Shijiazhuang Railway University（Social Sciences Edition）, 14, 14-20(2020).

[24] TASHIPULAT T. Research on improving the accuracy of land cover classification maps using fractal feature quantities. Environmental Remote Sensing, 150-160(1994).

[25] WANG Shaohua, WANG Fei, HE Meixuan. Spatial effect analysis of green industrial development in Beijing Tianjin Hebei Region： Supply side factor analysis based on dynamic SDM. Journal of Yanshan University（Philosophy and Social Sciences Edition）, 23, 88-96(2022).

[26] WANG Shuwei, CUI Wantian. Evaluation of coordinated economic development in the Beijing Tianjin Hebei Region. Business Economics Research, 162-166(2022).

[27] LIU Jiyuan, ZHANG Zengxiang, XU Xinliang et al. Spatial pattern and driving force analysis of land use change in China at the beginning of the 21st century. Journal of Geography, 64, 1411-1420(2009).

[28] LIU Jiyuan, ZHANG Zengxiang, ZHUANG Dafang et al. Spatial and temporal characteristics and causes of land use change in China in the 1990s. Geographic Research, 1-12(2003).

[29] LI Sihui. Research on land cover classification and monitoring of changes in returning farmland to forests and grasslands in the Eastern Mongolian Region based on MODIS(2010).

[30] ZHAO Huiqing, Guangqing LÜ. Analysis of high quality economic development and coupling coordination in the Beijing Tianjin Hebei Region. Journal of Tianjin University of Commerce, 42, 32-39，55(2022).

[31] ZHOU Huan. The era significance of promoting economic synergy and low-carbon synergy in the Beijing Tianjin Hebei region. New Economy, 54-59(2022).

[32] YANG Congcong, TAO Yong, QI Jiuhan. Research on the impact of environmental factors on the accuracy of land cover classification. Comprehensive Utilization of Resources in China, 39, 51-54(2021).

[33] TIAN Xuebing, LU Yan. Under the new development pattern， the deep integration of the innovation chain of the Beijing Tianjin Hebei industrial chain promotes the high-quality development of industries in Hebei Province - Summary of the 2021 Beijing Tianjin Hebei Collaborative Development Consultant Seminar. Journal of the Shijiazhuang Municipal Party Sc-hool of the Communist Party of China, 24, 39-44(2022).

[34] DANG Tao, LI Yani, LUO Junkai et al. Object oriented remote sensing image classification based on minimum distance method. Surveying and Spatial Geographic Information, 40, 163-165，169，173(2017).

[35] HU Luanyun, CHEN YanLei, XU Yue et al. 30m resolution remote sensing mapping of land cover in China based on fast clustering method. Chinese Science： Earth Science, 44, 1621-1633(2014).

[36] CHEN Jun, CHEN Jin. GlobeLand30 remote sensing mapping innovation and big data analysis. Chinese Science： Earth Science, 48, 1391-1392(2018).

[37] JIANG Lufan, SU Huiyi, ZHANG Yin et al. Research on land cover classification and prediction based on multi classi-fier ensemble. Journal of Nanjing Forestry University （Natural Science Edition）, 48, 255-266(2024).

[38] XIE Zhiwei, ZHAI Shuaizhi, ZHANG Fengyuan et al. Object oriented high-resolution image inductive graph neural network classification method. Journal of Surveying and Mapping, 53, 1610-1623(2024).