[1] Akiyama, T., Kubota, J., Fujita, K. et al. (2018). Use of Water Balance and Tracer-Based Approaches to Monitor Groundwater Recharge in the Hyper-Arid Gobi Desert of Northwestern China. Environments, 5, 55. https://doi.org/10.3390/environments5050055
[2] Chen, T. B., Wong, J. W. C., Zhou, H. Y., & Wong, M. H. (1997). Assessment of Trace Metal Distribution and Contamination in Surface Soils of Hong Kong. Am. J. Infect. Control, 96, 61-68. https://doi.org/10.1016/S0269-7491(97)00003-1
[3] Chen, Y. J., Chen, Y. N., Liu, J. Z. et al. (2009). Influence of Intermittent Water Releases on Groundwater Chemistry at the Lower Reaches of the Tarim River, China. Environmental Monitoring & Assessment, 158, 251-264. https://doi.org/10.1007/s10661-008-0579-9
[4] Chen, Y., Zhang, X., Zhu, X. et al. 2004 (). Analysis on the Ecological Benefits of the Stream Water Conveyance to the Dried-Up River of the Lower Reaches of Tarim River, China. Science in China Series D: Earth Sciences, 47, 1053-1064. https://doi.org/10.1360/03yd0101
[5] Dee, D. P. et al. (2011) The ERA-Interim Reanalysis: Configuration and Performance of the Data Assimilation System. Quarterly Journal of the Royal Meteorological Society, 137, 553-597.
[6] Fawaz, H. I., Forestier, G., Weber, J. et al. (2019). Deep Learning for Time Series Classification: A Review. Data Mining and Knowledge Discovery, 33, 917-963. https://doi.org/10.1007/s10618-019-00619-1
[7] Garg, D., Goel, P., Kandaswamy, G., Ganatra, A., & Kotecha, K. (2018). A Roadmap to Deep Learning: A State-of-the-Art Step Towards Machine Learning. Advanced Informatics for Computing Research, Springer, Cham. https://doi.org/10.1007/978-981-13-3140-4_15
[8] Goodrich, B. A., Koski, R. D., & Jacobi, W. R. (2009). Condition of Soils and Vegetation Along Roads Treated with Magnesium Chloride for Dust Suppression. Water, Air, and Soil Pollution, 198, 165-188. https://doi.org/10.1007/s11270-008-9835-4
[9] Guo, G., Wu, F., Xie, F., & Zhang, R. (2012). Spatial Distribution and Pollution Assessment of Heavy Metals in Urban Soils from Southwest China. J. Environ. Sci., 24, 410-418.
[10] Gutman, G., & Ignalov, A. (1998). The Derivation of the Green Vegetation Fraction from NOAA/AVHRR Data for Use in Numerical Weather Prediction Models. Int. J. Remote Sens., 19, 1533-1543. https://doi.org/10.1080/014311698215333
[11] Hodges, K. I. et al. (2010). A Comparison of Extratropical Cyclones in Recent Reanalyses ERA-Interim, NASA MERRA, NCEP CFSR, and JRA-25. Journal of Climate, 24, 4888-4906. https://doi.org/10.1175/2011JCLI4097.1
[12] Huo, X. N., Li, H., Sun, D. F., Zhang, W. W., Zhou, L. D., & Li, B. G. (2010). Spatial Autogression Model for Heavy Metals in Cultivated Soils of Beijing. Trans. CSAE., 26, 78-82. (In Chinese)
[13] Jiapaer, G., Chen, X., & Bao, A. (2011). A Comparison of Methods for Estimating Fractional Vegetation Cover in Arid Regions. Agricultural and Forest Meteorology, 151, 1698-1710. https://doi.org/10.1016/j.agrformet.2011.07.004
[14] Lee, C. S., Li, X. D., Shi, W. Z., Cheung, S. C., & Thornton, I. (2006). Metal Contamination in Urban, Suburban, and Country Park Soils of Hong Kong: A Study Based on GIS and Multivariate Statistics. Sci. Total Environ., 356, 45-61. https://doi.org/10.1016/j.scitotenv.2005.03.024
[15] Li, X. D., Lee, S. L., Wong, S., Shi, W. Z., & Thornton, I. (2004). The Study of Metal Contamination in Urban Soils of Hong Kong Using a Gis-Based Approach. Environ. Pollution., 129, 113-124. https://doi.org/10.1016/j.envpol.2003.09.030
[16] Li, X. W., Xie, Y. F., Wang, J. F., Christakos, G., Si, J. L., Zhao, H. N., Ding, H. N., & Li, J. (2013). Influence of Planting Patterns on Fluoroquinolone Residues in the Soil of an Intensive Vegetable Cultivation Area in Northern China. Sci. Total Environ., 458-460, 63-69. https://doi.org/10.1016/j.scitotenv.2013.04.002
[17] Lin, Y. P., Teng, T. P., & Chang, T. K. (2002). Multivariate Analysis of Soil Heavy Metal Pollution and Landscape Pattern in Chuanghuacounty in Taiwan. Landsc. Urban Plan., 62, 19-35. https://doi.org/10.1016/S0169-2046(02)00094-4
[18] Luo, W., Jasiewicz, J., Stepinski, T., Wang, J. F., Xu, C. D., & Cang, X. Z. (2015). Spatial Association be-tween Dissection Density and Environmental Factors over the Entire Conterminous United States. Geophys. Res. Lett., 43, 692-700. https://doi.org/10.1002/2015GL066941
[19] Navas, A., & Machin, J. (2002). Spatial Distribution of Heavy Metals and Arsenic in Soils of Aragon (Northeast Spain): Controlling Factors and Environmental Implications. Appl.Geochem., 17, 961-973. https://doi.org/10.1016/S0883-2927(02)00006-9
[20] Ordonez, A., Loredo, J., De Miguel, E., & Charlesworth, S. (2003). Distribution of Heavy Metals in the Street Dusts and Soil of an Industrial City in Northern Spain. Arch. Environ.Contam. Toxicol., 44, 160-170. https://doi.org/10.1007/s00244-002-2005-6
[21] Petley, D. (2012). Global Patterns of Loss of Life from Landslides. Geology, 40, 927-930. https://doi.org/10.1130/G33217.1
[22] Semwal, V. B., Mondal, K., & Nandi, G. C. (2017). Robust and Accurate Feature Selection for Humanoid Push Recovery and Classification: Deep Learning Approach. Neural Computing and Applications, 28, 565-574. https://doi.org/10.1007/s00521-015-2089-3
[23] Shi, T. Z., Hu, Z. W., Shi, Z. et al. (2018). Geo-Detection of Factors Controlling Spatial Patterns of Heavy Metals in Urban Topsoil Using Multi-Source Data. Science of the Total Environment, 643, 451-459. https://doi.org/10.1016/j.scitotenv.2018.06.224
[24] Sun, C. Y., Liu, J., Wang, Y., Sun, L., & Yu, H. (2013). Multivariate and Geostatistical Analyses of the Spatial Distribution and Sources of Heavy Metals in Agricultural Soil in De-hui. Northeast China. Chemosphere, 92, 517-523. https://doi.org/10.1016/j.chemosphere.2013.02.063
[25] Sylla, M. B. et al. (2010). Multiyear Simulation of the African Climate Using a Regional Climate Model (RegCM3) with the High Resolution ERA-Interim Reanalysis. Climate Dynamics, 35, 231-247. https://doi.org/10.1007/s00382-009-0613-9
[26] Tang, S.-H., Zhu, Q.-J., Zhou, Y.-Y., Bai, X.-H., & Shuai, Y.-M. (2003). A Simple Method to Estimate Crown Cover Fraction and Rebuild the Background Information. J. Image Graphics, 8, 1304-1309. (In Chinese)
[27] Tokola, T. (2015). Remote Sensing Concepts and Their Applicability in REDD+ Monitoring. Current Forestry Reports, 1, 252-260. https://doi.org/10.1007/s40725-015-0026-4
[28] Tyr, W. H., Stewart, E. L., Nicholas, K. et al. (2018). Image Set for Deep Learning: Field Images of Maize Annotated with Disease Symptoms. BMC Research Notes, 11, Article Number: 440. https://doi.org/10.1186/s13104-018-3548-6
[29] Wang, J. F., & Xu, C. D. (2017). Geodetector: Principle and Prospective. ActaGeograph. Sin., 72, 116-134.
[30] Wang, J. F., Li, X. H., Christakos, G., Liao, Y. L., Zhang, T., Gu, X., & Zhang, X. Y. (2010). Geographical Detectors-Based Health Risk Assessment and its Application in the Neural Tube Defects Study of the Heshun Region, China. Int. J. Geogr. Inf. Syst., 24, 107-127. https://doi.org/10.1080/13658810802443457
[31] Wang, J. F., Zhang, T. L., & Fu, B. J. (2016). A Measure of Spatial Stratified Heterogeneity. Ecol.Indic., 67, 250-256. https://doi.org/10.1016/j.ecolind.2016.02.052
[32] Wang, K. M., Qian, J., Huang, S. Y., Wang, Y., Yang, X. B., & Duo, B. (2019). Through-the-Wall Radar Imaging Based on Deep Learning. Social Informatics and Telecommunications Engineering, Springer, Cham. https://doi.org/10.1007/978-3-030-19156-6_58
[33] Weedon, G. P. et al. (2015). The WFDEI Meteorological Forcing Data Set: WATCH Forcing Data Methodology Applied to ERA-Interim Reanalysis Data. Water Resources Research, 50, 7505-7514. https://doi.org/10.1002/2014WR015638
[34] Wilford, J., de Caritat, P., & Bui, E. (2016). Predictive Geochemical Mapping Using Environmental Correlation. Appl. Geochem., 66, 275-288. https://doi.org/10.1016/j.apgeochem.2015.08.012
[35] Yabuki, N., Nishimura, N., & Fukuda, T. (2018). Automatic Object Detection from Digital Images by Deep Learning with Transfer Learning. Workshop of the European Group for Intelligent Computing in Engineering. Springer, Cham. https://doi.org/10.1007/978-3-319-91635-4_1
[36] Yang, Q., Mu, H., Wang, H. et al. (2018). Quantitative Evaluation of Groundwater Recharge and Evaporation Intensity with Stable Oxygen and Hydrogen Isotopes in a Semi-Arid Region, Northwest China. Hydrological Processes. https://doi.org/10.1002/hyp.11474
[37] Ye, Z. X., Chen, Y. N., & Li, W. H. (2010). Ecological Water Demand of Natural Vegetation in the Lower Tarim River. Journal of Geographical Sciences, 20, 261-272. https://doi.org/10.1007/s11442-010-0261-3
[38] Yu, S., Yuan, L., Guan, W. et al. (2017). Deep Learning for Plant Identification in Natural Environment . Computational Intelligence and Neuroscience, 2017, 1-6. https://doi.org/10.1155/2017/7361042
[39] Zhang, R., Li, Z., Sun, X. et al. (2005). On the Applicability of Kirchoff’s Law and the Principle of Heat Balance in Thermal Infrared Remote Sensing: A Non-Isothermal System. Science in China, 48, 53-64. https://doi.org/10.1360/03YD0108