关键词: font-size:10.5pt, ">风云卫星font-size:10.5pt, ">, font-size:10.5pt, ">微波遥感font-size:10.5pt, ">, font-size:10.5pt, ">土壤湿度font-size:10.5pt, ">, font-size:10.5pt, ">适用性分析

Abstract:

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 are highly 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 microwave radiation imagers (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 Soil Moisture Active and Passive (SMAP) and Soil Moisture Ocean Salinity (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. The grids of satellites are transformed into longitudes and latitudes using Equal-Area Scalable Earth Grid (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⁻³ and >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 R less than 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. The correlation and estimated error 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 retrievals in the footprints. The X-band (band used by MWRI) detection depth is relatively shallow, and the retrievals are more affected by surface vegetation; while the 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.

Key words: font-family:等线, font-size:10.5pt, ">Fengyun satellitefont-family:等线, font-size:10.5pt, ">, font-family:等线, font-size:10.5pt, ">Sfont-family:等线, font-size:10.5pt, ">oilfont-family:等线, font-size:10.5pt, "> , font-family:等线, font-size:10.5pt, ">moisturefont-family:等线, font-size:10.5pt, ">, font-family:等线, font-size:10.5pt, "> , Mfont-family:等线, font-size:10.5pt, ">icrowavefont-family:等线, font-size:10.5pt, "> , font-family:等线, font-size:10.5pt, ">remotefont-family:等线, font-size:10.5pt, "> , font-family:等线, font-size:10.5pt, ">sensingfont-family:等线, font-size:10.5pt, ">, font-family:等线, font-size:10.5pt, ">Applicability analysis