参考文献：

[1] Bai, L. L., Long, D., Yan, L. Estimation of Surface Soil Moisture With Downscaled Land Surface Temperatures Using a Data Fusion Approach for Heterogeneous Agricultural Land [J]. Water Resources Research, 2019, 55(2): 1105-1128.
     [2] 郑超磊, 胡光成, 陈琪婷等. 遥感土壤水分对蒸散发估算的影响[J]. 遥感学报, 2021, 25(04): 990-999.
     [3] 谢秋霞, 贾立, 陈琪婷等. 闪电河流域农牧交错带微波遥感土壤水分产品评价[J]. 遥感学报, 2021, 25(04): 974-989.
     [4] Merlin, O., Escorihuela, M.J., Mayoral, M.A., et al. Self-calibrated evaporation-based disaggregation of SMOS soil moisture: An evaluation study at 3 km and 100 m resolution in Catalunya, Spain [J]. Remote Sensing of Environment, 2013, 130: 25-38.
     [5] Chan, S. K., Bindlish, R., O'Neill, P., et al. Development and assessment of the SMAP enhanced passive soil moisture product [J]. Remote Sensing of Environment, 2018, 204: 2539-2542.
     [6] Das, N. N., Entekhabi, D., Dunbar, R.S., et al. The SMAP mission combined active-passive soil moisture product at 9 km and 3 km spatial resolutions [J]. Remote Sensing of Environment, 2018, 211: 204-217.
     [7] Zribi, M., Andre, C., Decharme, B. A method for soil moisture estimation in Western Africa based on ERS Scatterometer [J]. IEEE Transactions on Geoscience and Remote Sensing, 2008, 46(2): 438-448.
     [8] Zribi, M., Gorrab, A., Baghdadi, N. A newsoil roughness parameter for themodelling of radar backscattering over bare soil [J]. Remote Sensing of Environment, 2014, 152: 62-73.
     [9] Gorrab, A., Zribi, M., Baghdadi, N., et al. Potential of X-Band TerraSAR-X and COSMO-SkyMed SAR data for the assessment of physical soil parameters [J]. Remote Sensing, 2015, 7(1): 747-766.
     [10] Gao, Q., Zribi, M., Escorihuela, M.J., et al. Synergetic use of Sentinel-1 and Sentinel-2 data for soil moisture mapping at 100 m resolution [J]. Sensors , 2017, 17(9): 1966.
     [11] Li, J., Wang, S. Using SAR-derived vegetation descriptors in a Water Cloud Model to improve soil moisture retrieval [J]. Remote Sensing, 2018, 10(9): 1370.
     [12] Bousbih, S., Zribi, M., El Hajj, M., et al. Soil moisture and irrigation mapping in a semi-arid region, based on the synergetic use of Sentinel-1 and Sentinel-2 data [J]. Remote Sensing, 2018, 10(12): 1953.
     [13] Amazirh, A., Merlin, O., Er-Raki, S., et al. Retrieving surface soil moisture at high spatio-temporal resolution from a synergy between Sentinel-1 radar and Landsat thermal data: A study case over bare soil [J]. Remote Sensing of Environment, 2018, 211: 321-337.
     [14] Bao, Y., Lin, L., Wu, S., et al. Surface soil moisture retrievals over partially vegetated areas from the synergy of Sentinel-1 and Landsat 8 data using a modified water-cloud model [J]. International Journal of Applied Earth Observation and Geoinformation, 2018, 72: 76-85.
     [15] Gao, Q., Zribi, M., Escorihuela M. et al. Irrigation Mapping Using Sentinel-1 Time Series at Field Scale [J]. Remote Sensing, 2018, 10(9): 1495.
     [16] 李建民, 王俊杰, 晏清洪. 山东省潘庄灌区水土资源平衡分析[J]. 水利发展研究, 2020, 20(9): 47-50+58.
     [17] 冯云强, 李庆玉, 王合军等. 山东潘庄灌区超声波水位系统组网方案探析[J]. 地下水, 2007 (4): 117-118.
     [18] 张东彦, 戴震, 徐新刚等. 基于时序Sentinel-2影像的现代农业园区作物分类研究[J]. 红外与激光工程, 2021, 50(05): 262-272.
     [19] Yu, F., Zhao, Y. A new semi-empirical model for soil moisture content retrieval by ASAR and TM data in vegetation-covered areas[J]. Science China Earth Sciences, 2011, 54(12): 1955-1964.