[1] FU Bojie, OUYANG Zhiyun, SHI Peng et al. Current condition and protection strategies of Qinghai-Tibet Plateau ecological security barrier. Bulletin of Chinese Academy of Sciences, 36, 1298-1306(2021).

[2] ZHAO X Q, ZHAO L, LI Q et al. Using balance of seasonal herbage supply and demand to inform sustainable grassland management on the Qinghai–Tibetan Plateau. Frontiers of Agricultural Science and Engineering, 5, 1(2018).

[3] FANG Jingyun, BAI Yongfei, LI Linghao et al. Scientific basis and practical ways for sustainable development of China’s pasture regions. Chinese Science Bulletin, 61, 155-164(2016).

[4] REN Jizhou, LIN Huilong. Promoting prataculture development in arable region to ameliorate the farming system and insure food security in China. Acta Prataculturae Sinica, 18, 1-9(2009).

[5] HU Zizhi. The importance of artificial grassland in the development of prataculture and the control of environment in China of 21 century. Grassland and Turf, 20, 12-15(2000).

[6] TIAN Lihua, ZHOU Qingping, WANG Jiating et al. Situations， issues and solutions of the grassland animal husbandry of Qinghai-Tibetan Plateau. Journal of Southwest University for Nationalities （Natural Science Edition）, 42, 119-126(2016).

[7] JIA Kun, LI Qiangzi, TIAN Yichen et al. A review of classification methods of remote sensing imagery. Spectroscopy and Spectral Analysis, 31, 2618-2623(2011).

[8] ZHANG W M, BRANDT M, PRISHCHEPOV A V et al. Mapping the dynamics of winter wheat in the North China Plain from dense Landsat time series （1999 to 2019）. Remote Sensing, 13, 1170(2021).

[9] GUO Y, XIA H M, ZHAO X Y et al. Early-season mapping of winter wheat and garlic in Huaihe basin using Sentinel-1/2 and Landsat-7/8 imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 16, 8809-8817(2022).

[10] YANG Liping, ZHANG Jing, GONG Enjun et al. Analysis of spatio-temporal land-use patterns and the driving forces in Xi’an City using GEE and multi-source data. Transactions of the Chinese Society of Agricultural Engineering, 38, 279-288(2022).

[11] ZHOU Tao, PAN Jianjun, HAN Tao et al. Planting area extraction of winter wheat based on multi-temporal SAR data and optical imagery. Transactions of the Chinese Society of Agricultural Engineering, 33, 215-221(2017).

[12] REN Haijuan, DONG Jianjun, LI Xiaoyuan et al. Extraction artificial alfalfa grassland information using Landsat8 remote sensing data. Chinese Journal of Grassland, 37, 81-87(2015).

[13] GUO Moran, LIU Tao, HAN Peng et al. Discriminating data of spacial distribution of artificial grassland based on multi-source satellite remote sensing date fusion. Chinese Journal of Grassland, 41, 53-62(2019).

[14] MA Zhanlin, LIU Changhua, XUE Huazhu et al. Identification of winter wheat by integrating active and passive remote sensing data based on Google Earth Engine platform. Transactions of the Chinese Society for Agricultural Machinery, 52, 195-205(2021).

[15] MAN Weidong, LI Chunjing. Study on the land use dynamic changes by the RS and GIS of Yan-Long-Tu area. Journal of Agricultural Science Yanbian University, 34, 21-26(2012).

[16] CHEN Quan, ZHOU Zhongfa, WANG Lingyu et al. Surface soil moisture retrieval using multi-temporal Sentinel-1 SAR data in Karst rocky desertification area. Journal of Infrared and Millimeter Waves, 39, 626-634(2020).

[17] ZHANG Guofei, YUE Cairong, ZHANG Wanqiu. Above-ground biomass estimation in Kunming Dianchi lake wetland using Sentinel imagery. Journal of Terahertz Science and Electronic Information Technology, 18, 142-149(2020).

[18] KUSSUL N, LEMOINE G, GALLEGO F J et al. Parcel-based crop classification in Ukraine using Landsat-8 data and Sentinel-1A data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9, 2500-2508(2016).

[19] PING Yuepeng, ZANG Shuying. Crop identification based on MODIS NDVI time-series data and phenological characteristics. Journal of Natural Resources, 31, 503-513(2016).

[20] BAI Yanying, GAO Julin, ZHANG Baolin. Extraction of crop planting structure based on time-series NDVI of Landsat8 images. Arid Land Geography, 42, 893-901(2019).

[21] LI Meili, YIN Lichang, ZHANG Yuan et al. Spatio-temporal dynamics of fractional vegetation coverage based on MODIS-EVI and its driving factors in Southwest China. Acta Ecologica Sinica, 41, 1138-1147(2021).

[22] LIU J X, HEISKANEN J, AYNEKULU E et al. Land cover characterization in West Sudanian Savannas using seasonal features from annual Landsat time series. Remote Sensing, 8, 365(2016).

[23] TAN Shen, WU Bingfang, ZHANG Xin. Mapping paddy rice in the Hainan Province using both Google Earth Engine and remote sensing images. Journal of Geo-Information Science, 21, 937-947(2019).

[24] LI Binbin, XIE Huan, TONG Xiaohua et al. Land cover classification using ICESat-2 data with random forest. Infrared and Laser Engineering, 49, 115-121(2020).

[25] LI Xuqing, LIU Shimeng, LI Long et al. Automatic interpretation of spatial distribution of winter wheat based on random forest algorithm to optimize multi-temporal features. Transactions of the Chinese Society for Agricultural Machinery, 50, 218-225(2019).

[26] LIU Jie, LIU Jikai, AN Jingjing et al. Precise crop classification based on multi-features from time-series Landsat 8 OLI images and Random Forest Algorithm. Agricultural Research in the Arid Areas, 38, 281-288(2020).

[27] MAXWELL A E, WARNER T A, FANG F. Implementation of machine-learning classification in remote sensing：An applied review. International Journal of Remote Sensing, 39, 2784-2817(2018).

[28] LIU Mingyue, ZHENG Hao, CHEN Xingtong et al. Extraction of invasive plant spartina alterniflora by combining vegetation phenological characteristics and machine learning supported by GEE. Bulletin of Surveying and Mapping, 36-43(2023).

[29] GORELICK N, HANCHER M, DIXON M et al. Google Earth Engine： Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, 18-27(2017).

[30] YOU Hui, SU Rongrui, XIAO Weiyu et al. Extraction of rape planting distribution information in Jianghan Plain based on MODIS EVI time series data. Remote Sensing for Land & Resources, 30, 173-179(2018).

[31] ZHU Yongji, YIN Feijian, WANG Han et al. Winter wheat iden-tification based on multiple features of Sentinel-2AMSI data and random forest method. Journal of Anhui Science and Technology University, 36, 25-31(2022).

[32] CAI W X, TIAN J Y, LI X J et al. A new multiple phenological spectral feature for mapping winter wheat. Remote Sensing, 14, 4529(2022).

[33] FARR T G, ROSEN P A, CARO E et al. The shuttle radar topography mission. Reviews of Geophysics, 45, DOI： 10.1029/2005RG000183(2007).

[34] DE JONG R, DE BRUIN S, DE WIT A et al. Analysis of monotonic greening and browning trends from global NDVI time-series. Remote Sensing of Environment, 115, 692-702(2011).

[35] LLOYD D. A phenological classification of terrestrial vegetation cover using shortwave vegetation index imagery. International Journal of Remote Sensing, 11, 2269-2279(1990).

[36] MAO D H, WANG Z M, DU B J et al. National wetland mapping in China： A new product resulting from object-based and hierarchical classification of Landsat-8 OLI images. ISPRS Journal of Photogrammetry and Remote Sensing, 164, 11-25(2020).

[37] BERA B, SAHA S, BHATTACHARJEE S. Estimation of forest canopy cover and forest fragmentation mapping using Landsat satellite data of silabati river basin （India）. Journal of Cartography and Geographic Information, 70, 181-197(2020).

[38] PELLETIER C, VALERO S, INGLADA J et al. Assessing the robustness of random forests to map land cover with high resolution satellite image time series over large areas. Remote Sensing of Environment, 187, 156-168(2016).

[39] XU F, LI Z F, ZHANG S Y et al. Mapping winter wheat with combinations of temporally aggregated Sentinel-2 and Landsat-8 data in Shandong Province， China. Remote Sensing, 12, 2065(2020).

[40] SCHUSTER C, SCHMIDT T, CONRAD C et al. Grassland habitat mapping by intra-annual time series analysis-Comparison of Rapid Eye and TerraSAR-X satellite data. International Journal of Applied Earth Observation and Geoinformation, 34, 25-34(2015).

[41] DRUSCH M, DEL BELLO U, CARLIER S et al. Sentinel-2： ESA’s optical high-resolution mission for GMES operational services. Remote Sensing of Environment, 120, 25-36(2012).

[42] GENG R F, JIN S G, FU B L et al. Object-based wetland classification using multi-feature combination of ultra-high spatial resolution multispectral images. Canadian Journal of Remote Sensing, 46, 784-802(2020).

[43] ZHANG Hui. Deep learning based crop weed identification research. Changjiang Information & Communications, 36, 25-28(2023).

[44] TENG Wenxiu, WEN Xiaorong, WANG Ni et al. Tree species classification and mapping based on deep transfer learning with unmanned aerial vehicle high resolution images. Laser & Optoelectronics Progress, 56(2019).

[45] LIU Tongxing, MA Mingxia, LU Changlun et al. Wheat identification by using deep learning algorithm from unmanned aerial vehicle[J]. Chinese Journal of Agricultural Resources and Regional Planning, 41, 150-158(2020).