The interactive multisensor snow and ice mapping system (IMS) of the National Oceanic and Atmospheric Administration combines multiple data sources to map northern hemisphere snow cover. IMS can identify snow cover beneath clouds using time series images from geostationary satellites and passive-microwave observations. During the snow disaster of 2008 in southern China, IMS snow-cover data were more accurate than those retrieved from passive-microwave remote sensing data and moderate resolution imaging spectroradiometer snow-cover products as compared with in situ measurements. The IMS snow-cover mapping accuracy was assessed against ground truth, which was derived using Landsat Enhanced Thematic Mapper Plus (ETM+) images. The actual snow cover was assessed from 47 ETM+ scenes that were obtained under mostly clear-sky conditions (cloud cover of <20%) from 2008 to 2011 and were subsequently used to evaluate the IMS snow-cover product. Land cover and terrain effects on the accuracy of snow-cover products were considered in this study. The IMS snow-cover product was consistent with the ETM+ snow images over flat surfaces, e.g., cropland, and the average agreement was greater than 85%. For forested, mountainous areas, a pronounced inconsistency was observed between the two datasets. The agreement of the IMS snow-cover product in these regions was <75%, and the IMS appeared to overestimate snow by over 50%. The sparser snow in 2009, 2010, and 2011 caused poorer accuracies and more severe overestimations. In addition, mixed pixels, particularly in complex terrain, have been recognized as a major problem that affects the accuracy of IMS snow detection because of the product’s coarse spatial resolution (i.e., 4 km). Specifically, fragmented snow cover is difficult to discern with 4-km pixels. Therefore, further studies are required to develop a fractional snow-cover algorithm for the IMS product.
Snow cover is an important parameter in the hydrological applications and global climate change research. Accurate
snow cover information in daily basis is significant in weather forecasting, hydrological model and other applications.
High temporal resolution of geostationary data can provide snow cover maps with less cloud obscuration. In this paper,
Fengyun-2 geostationary satellites (FY-2D and FY-2E) and Multi-functional Transport Satellite-2 (MTSAT-2) data were
compared and used in snow cover mapping over China. FY-2D, FY-2E and MTSAT-2 data calibrated by GSICS was
compared firstly. Then we used the same snow cover algorithm to test the performance of the three geostationary
satellites on January and February, 2013 over China. Meteorological station observations were utilized to validate the
snow cover maps of FY-2D, FY-2E and MTSAT-2. Results indicated that FY-2D and FY-2E presented similar and good
performance over China, with overall accuracy about 92%. On the other hand, the overall accuracy of MTSAT-2 was
approximately 88%, which was lower than FY-2D and FY-2E. Further calibration of the MTSAT-2 data with FY-2D/E
should be considered in future study.
Soil moisture is an important parameter in hydrological circulation. For the microwave signal at L-band is very sensitive
to the soil moisture, there have been many algorithms to retrieve soil moisture at L-band. The Soil Moisture and Ocean
Salinity (SMOS) mission is launched in 2009, and the surface soil moisture retrieving is based on the inversion of the Lband
Microwave Emission of the Biosphere (L-MEB) radiative transfer model. Due to the heterogeneity of the surface,
the capability of the model remains to be verified in some region. In the study, the brightness temperature at L-band in
Heihe River Basin is simulated by using the τ-ω model firstly. Secondly, the sensitivity analysis of the model on the
parameters is conducted to get the optimal results. At last, the simulated brightness temperature is calculated by using the
adjusted parameters, and the PLMR microwave brightness temperature is used to validate the simulation results. It turns
out that the root-mean-square errors between L-MEB simulated and PLMR are 9K to 12K for V-polarization, and 6K to
8K at H-polarization respectively at different angles, which proves the L-MEB model have an good capability in the of
China.
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