风云三号卫星微波遥感土壤水分产品在山东地区的适用性分析

风云卫星微波遥感土壤湿度产品在农业应用中，尤其是在作物监测和干旱预警中发挥着重要作用，对其产品可靠性的评估至关重要。本研究以中国气象局的自动土壤水分观测站土壤湿度数据为参考，系统分析了山东地区2018年FY-3B和FY-3C卫星反演2级土壤湿度产品的质量及其时空分布，并与SMAP和SMOS卫星反演3级土壤湿度产品进行对比。使用EASE-Grid投影方法的转换方程进行空间匹配，将卫星的格点转换成经纬度后，自动站对应的卫星观测结果由其周围4个观测格点结果加权平均得到。对自动站数据去除异常值后，将卫星过境时刻数据与自动站小时数据进行时间匹配。结果表明，在山东地区FY-3B、FY-3C和SMAP与自动站观测数据时间一致性较好，均方根误差（RMSE）约0.09m³·m^?3，相关系数（R）大于0.3。SMAP无偏均方根误差（ubRMSE）可以达到0.05m³·m^?3，说明其去除系统误差之后有较高的应用价值，而SMOS在山东地区的适用性不如SMAP。同时，FY-3B、FY-3C和自动站的相关性和误差有明显的季节变化，FY-3B和FY-3C往往高估5、8和9月的土壤湿度，与冬小麦和夏季玉米的成熟期对应，而在其余时间会低估，这可能是因为风云卫星使用的X波段探测深度较浅，其结果受表层植被的影响较大；而SMAP和SMOS使用的L波段探测较深，其结果受表层植被影响较小。该发现说明，未来风云卫星土壤湿度的反演算法可以通过优化植被的影响来获得更精确的反演结果。

Analysis on the Applicability of Fengyun-3Satellite Microwave Remote Sensing Soil Moisture Products in Shandong

Soil moisture (SM) is one of the fundamental variables in the global energy and water cycles.Among various measurements, satellite retrieved soil moisture products are playing an increasingly important role in applications such as meteorology, hydrology, climatology, agriculture, and so on, because accurate measurements of soil moisture on large scales arehighly helpful in crop yield estimation, drought prediction and disaster monitoring in agricultural regions, particularly in arid and semiarid areas. Fengyun-3 (FY-3) satellite series is current Chinese second-generation polar-orbiting meteorological satellite series for weather forecast, climate prediction and environmental monitoring. The microwaveradiationimagers (MWRI) onboard both FY-3B and FY-3C are widely used for retrievals of ocean surface wind speed and temperature, liquid water content in clouds, precipitation intensity, water vapor content and soil moisture. To understand the applicability and performance of MWRI-based soil moisture products, this study systematically analyzes the FY-3B and FY-3C operational soil moisture products over Shandong province, China in an entire year of 2018. Shandong province is a typical agricultural region, and the surface vegetation water content largely depends on the agricultural growth. Similar satellite-based results from microwave radiometers onboard the SoilMoistureActiveandPassive(SMAP) and SoilMoistureOceanSalinity(SMOS) are also considered for comparison and evaluation. The ground-based soil moisture observations from the China Automatic Soil Moisture Observation Stations (CASMOS) of the Chinese Meteorological Administration are used as references, and only the soil moisture of the upper soil layer (0?10cm) is chosen. For a fair comparison, the satellite-based datasets are collocated with the ground-based CASMOS ones in time and space, and abnormal values from the CASMOS are removed. The automatic station hourly data at the time of the satellites ascending and descending are chosen. Thegrids of satellites are transformed into longitudes and latitudes using Equal-AreaScalableEarthGrid(EASE-grid) formula, and then matched with corresponding CASMOS stations. The average difference (AD), root mean square error (RMSE), unbiased RMSE (ubRMSE) and the correlation coefficient (R) are calculated to quantify satellite products’ reliability. The temporal series of regional average soil moisture from four satellites (i.e., FY-3B, FY-3C, SMAP and SMOS) and CASMOS are compared. The statistical parameters between satellite-based and ground-based soil moistures from each stations are calculated, and the corresponding spatial variations are discussed. Our results show that FY-3B, FY-3C and SMAP have relatively higher correlations with the ground-based data on the temporal scale in Shandong Province, and the RMSE and R values are 0.09m³·m^?3and >0.3, respectively. The ubRMSE of SMAP is approximately 0.05m³·m^?3, indicating that it will have a much improved accuracy after the systematic errors are removed, while the accuracy of SMOS in Shandong is slightly worse with Rlessthan 0.2. For the spatial distribution, the average difference of FY-3 products from the CASMOS results is negative in the west region of Shandong and positive in the east region, and, in other words, the FY-3 results are drier in west and wetter in east. Meanwhile, results from only over 60% automatic stations have correlation coefficients larger than 0.3. Thecorrelation and estimatederror between FY-3 products and ground-based data have obvious seasonal variations. FY-3B and FY-3C tend to overestimate the soil moisture in May, August, and September, corresponding to the maturity period of winter wheat and summer corn, and to underestimate the soil moisture during the rest of the year. The correlation coefficients between NDVI and average difference of FY-3B and FY-3C are 0.79 and 0.76, respectively, much higher than SMAP (0.54) and SMOS (?0.18). These results agree with our expectations, because vegetation biomass considerably influences passive microwave soil moisture retrievalsinthefootprints. The X-band (band used by MWRI) detection depth is relatively shallow, and the retrievals are more affected by surface vegetation; whilethe L-band (band used by radiometer) detection depth is deeper, and the retrievals are less affected by surface vegetation. It can be seen that in the future, Fengyun satellites can optimize the influence of vegetation in the soil moisture retrieval algorithm to obtain more accurate results.