An improved algorithm for retrieving thin sea ice thickness in the Arctic Ocean from SMOS and SMAP L-band radiometer data

The aim of this study was to develop an improved thin sea ice thickness(SIT)retrieval algorithm in the Arctic Ocean from the Soil Moisture Ocean Salinity and Soil Moisture Active Passive L-band radiometer data.This SIT retrieval algorithm was trained using the simulated SIT from the cumulative freezing degree days model during the freeze-up period over five carefully selected regions in the Beaufort,Chukchi,East Siberian,Laptev and Kara seas and utilized the microwave polarization ratio(PR)at incidence angle of 40°.The improvements of the proposed retrieval algorithm include the correction for the sea ice concentration impact,reliable reference SIT data over different representative regions of the Arctic Ocean and the utilization of microwave polarization ratio that is independent of ice temperature.The relationship between the SIT and PR was found to be almost stable across the five selected regions.The SIT retrievals were then compared to other two existing algorithms(i.e.,UH_SIT from the University of Hamburg and UB_SIT from the University of Bremen)and validated against independent SIT data obtained from moored upward looking sonars(ULS)and airborne electromagnetic(EM)induction sensors.The results suggest that the proposed algorithm could achieve comparable accuracies to UH_SIT and UB_SIT with root mean square error(RMSE)being about 0.20 m when validating using ULS SIT data and outperformed the UH_SIT and UB_SIT with RMSE being about 0.21 m when validatng using EM SIT data.The proposed algorithm can be used for thin sea ice thickness(<1.0 m)estimation in the Arctic Ocean and requires less auxiliary data in the SIT retrieval procedure which makes its implementation more practical.

Understanding root-zone soil moisture in agricultural regions of Central Mexico using the ensemble Kalman filter,satellite-derived information,and the THEXMEX-18 dataset

An Ensemble Kalman Filter(EnKF)-based assimilation algorithm was implemented to estimate root-zone soil moisture(RZSM)using a Soil-Vegetation-Atmosphere Transfer(SVAT)model during a complete growing season of corn in Central Mexico.Synthetic and field soil moisture(SM)observations and NASA SMAP SM retrievals were used to understand the effect of vertically spatial updates and uncertainties in meteorological forcings on RZSM estimates.Assimilation of RZSM every 3 days using SM observations at 4 depths lowered the averaged standard deviation(ASD)and the root mean square error(RMSE)by 60%and 50%,respectively,compared to the open-loop ASD.The assimilation of synthetic SM at the top 0-5 cm obtained RZSM closer to observations compared to THEXMEX-18 SM measurements and SMAP SM retrievals.Differences between EnKF estimates and SM observations and SMAP SM retrievals are mainly due to misrepresentation of vegetation conditions.The results improved SM estimates up to 10-cm depth using SMAP SM retrievals;however,additional studies are needed to improve SM at deeper layers.The implemented methodology can estimate SM at the top 10 cm of the soil every 3 days to mitigate the impact of the climate change on agricultural production over rainfed areas,particularly in developing countries.

闪电河流域农牧交错带微波遥感土壤水分产品评价

空间网格分辨率为9 km的SMAP(Soil Moisture Active and Passive)、0.1D(Degree)的ASCAT(The Advanced Scatterometer)、25 km的FY-3B以及25 km ESA-CCI(European Space Agency-Climate Change Initiative)是较为广泛应用的卫星遥感土壤水分产品,对数据质量的评价是进一步应用于旱情监测、蒸散发估算等研究的前提。本研究基于2018年9月在闪电河流域内蒙古农牧交错带区域开展的碳、水循环与能量平衡遥感综合试验,采用近似同步的两种尺度观测数据即点尺度地面实测土壤水分数据以及面尺度(1 km×1 km)机载土壤水分数据,利用RMSE(Root Mean Square Error),MAE(Mean Absolute Error),R(Correlation Coefficient),Bias以及ubRMSE(unbiased Root Mean Square Error)等评价指标分别对SMAP,ASCAT,FY-3B,ESA-CCI土壤水分卫星遥感产品进行了评价。本研究利用机载土壤水分数据作为桥梁,实现了从点尺度地面实测土壤水分数据、至面尺度(1 km×1 km)机载土壤水分数据、再至粗格网面尺度(9 km×9 km、0.1 D×0.1 D、25 km×25 km)卫星遥感土壤水分产品的对比分析过程。利用地面观测值对机载观测土壤水分开展评价分析,发现在裸土区域,机载土壤水分数据与地面实测数据较为一致,RMSE,MAE,Bias,ubRMSE以及R值分别为0.033 cm^(3)/cm^(3),0.030 cm^(3)/cm^(3),-0.004 cm^(3)/cm^(3),0.033 cm^(3)/cm^(3),0.474。对卫星土壤水分产品的评价结果显示,SMAP的9 km土壤水分卫星产品与地面观测更为一致,其RMSE,MAE,Bias,ubRMSE以及R值分别为0.037 cm^(3)/cm^(3),0.032 cm^(3)/cm^(3),-0.008 cm^(3)/cm^(3),0.036 cm^(3)/cm^(3),0.507。SMAP,ASCAT,FY-3B以及ESA-CCI与机载土壤水分数据有更高的相关性,R值分别为0.735,0.558,0.558,0.575。综上,闪电河流域实验区内的4种卫星遥感土壤水分产品中,SMAP产品与地面土壤水分、机载土壤水分数据均较为一致,其次是FY-3B与ESA-CCI。