Roughness-induced emission from ocean surfaces is one of the main issues that affects the retrieval accuracy of sea surface salinity remote sensing.In previous studies,the correction of roughness effect mainly depende...Roughness-induced emission from ocean surfaces is one of the main issues that affects the retrieval accuracy of sea surface salinity remote sensing.In previous studies,the correction of roughness effect mainly depended on wind speeds retrieved from scatterometers or those provided by other means,which necessitates a high requirement for accuracy and synchronicity of wind-speed measurements.The aim of this study is to develop a novel roughness correction model of ocean emissivity for the salinity retrieval application.The combined active/passive observations of normalized radar cross-sections(NRCSs)and emissivities from ocean surfaces given by the L-band Aquarius/SAC-D mission,and the auxiliary wind directions collocated from the National Centers for Environmental Prediction(NCEP)dataset are used for model development.The model is validated against the observations and the Aquarius standard algorithms of roughness-induced emissivity correction.Comparisons between model computations and measurements indicate that the model has better accuracy in computing wind-induced brightness temperature in the upwind/downwind directions or for the surfaces with smaller NRCSs,which can be better than 0.3 K.However,for crosswind directions and larger NRCSs,the model accuracy is relatively low.A model using HH-polarized NRCSs yields better accuracy than that using VV-polarized ones.For a fair comparison to the Aquarius standard algorithms using wind speeds retrieved from multi-source data,the maximum likelihood estimation is employed to produce results combining our model calculations and those using other sources.Numerical simulations show that combined results basically have higher accuracy than the standard algorithms.展开更多
Soil moisture is an important factor in global hydrologic circulation and plays a significant role in the research of hydrology, climatology, and agriculture. Microwave remote sensing is less limited by climate and ti...Soil moisture is an important factor in global hydrologic circulation and plays a significant role in the research of hydrology, climatology, and agriculture. Microwave remote sensing is less limited by climate and time, and can measure in large scale. With these characteristics, this technique becomes an effective tool to measure soil moisture. Since the 1980s, Chinese researchers have investigated the soil moisture using microwave instruments. The active re- mote sensors are characteristic of high spatial resolution, thus with launch of a series of satellites, active microwave remote sensing of soil moisture will be emphasized. The passive microwave remote sensing of soil moisture has a long research history, and its retrieval algorithms were developed well, so it is an important tool to retrieve large scale moisture information from satellite data in the future.展开更多
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 SI...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.展开更多
土壤水分是控制陆地和大气间水热能量交换的关键因子之一,在地球生态系统中起着重要的作用。定量化获取土壤水分信息对农业生产、应对全球变化、保护生态环境等众多领域都有着重要意义。目前,获取精度较高的大区域土壤湿度信息依然是研...土壤水分是控制陆地和大气间水热能量交换的关键因子之一,在地球生态系统中起着重要的作用。定量化获取土壤水分信息对农业生产、应对全球变化、保护生态环境等众多领域都有着重要意义。目前,获取精度较高的大区域土壤湿度信息依然是研究的热点和难点问题。气候变化倡议项目（climate change initiative,CCI）土壤湿度数据集是由多种主、被动微波数据融合的大尺度土壤湿度数据集,对其在中国区域的数据质量改进具有较高的实际应用价值。研究利用累积概率分布函数（cumulative distribution function,CDF）匹配方法对CCI土壤湿度产品进行改进。选择有较多实测数据的河北、山西、天津等部分区域,获得2009-2010年每月三旬（共72旬）的土壤湿度插值数据,以此为基础利用CDF进行重调整,建立逐像元的CCI土壤湿度数据的改进模型;然后利用站点实测数据进行该方法的有效性验证。结果表明,CDF调整前的偏差、均方根偏差和平均相对误差分别集中在0.05-0.09、0.05-0.1、0.20-0.45,调整后分别降低在0-0.04、0-0.05、0-0.2范围。可见,CDF调整后的误差明显减小,调整后的CCI土壤湿度的精度得到了明显的提高。展开更多
基金The National Key R&D Program of China under contract Nos 2018YFA0605403 and 2016YFB0500204the Hainan Provincial Natural Science Foundation of China under contract No.418QN301the National Natural Science Foundation of China under contract No.41801238。
文摘Roughness-induced emission from ocean surfaces is one of the main issues that affects the retrieval accuracy of sea surface salinity remote sensing.In previous studies,the correction of roughness effect mainly depended on wind speeds retrieved from scatterometers or those provided by other means,which necessitates a high requirement for accuracy and synchronicity of wind-speed measurements.The aim of this study is to develop a novel roughness correction model of ocean emissivity for the salinity retrieval application.The combined active/passive observations of normalized radar cross-sections(NRCSs)and emissivities from ocean surfaces given by the L-band Aquarius/SAC-D mission,and the auxiliary wind directions collocated from the National Centers for Environmental Prediction(NCEP)dataset are used for model development.The model is validated against the observations and the Aquarius standard algorithms of roughness-induced emissivity correction.Comparisons between model computations and measurements indicate that the model has better accuracy in computing wind-induced brightness temperature in the upwind/downwind directions or for the surfaces with smaller NRCSs,which can be better than 0.3 K.However,for crosswind directions and larger NRCSs,the model accuracy is relatively low.A model using HH-polarized NRCSs yields better accuracy than that using VV-polarized ones.For a fair comparison to the Aquarius standard algorithms using wind speeds retrieved from multi-source data,the maximum likelihood estimation is employed to produce results combining our model calculations and those using other sources.Numerical simulations show that combined results basically have higher accuracy than the standard algorithms.
基金Under the auspices of Knowledge Innovation Program of Chinese Academy of Sciences (No. KZCX2-309)
文摘Soil moisture is an important factor in global hydrologic circulation and plays a significant role in the research of hydrology, climatology, and agriculture. Microwave remote sensing is less limited by climate and time, and can measure in large scale. With these characteristics, this technique becomes an effective tool to measure soil moisture. Since the 1980s, Chinese researchers have investigated the soil moisture using microwave instruments. The active re- mote sensors are characteristic of high spatial resolution, thus with launch of a series of satellites, active microwave remote sensing of soil moisture will be emphasized. The passive microwave remote sensing of soil moisture has a long research history, and its retrieval algorithms were developed well, so it is an important tool to retrieve large scale moisture information from satellite data in the future.
基金The National Natural Science Foundation of China under contract Nos 41830536 and 41925027the Guangdong Natural Science Foundation under contract No.2023A1515011235the Innovation Group Project of Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)under contract No.311021008.
文摘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.
文摘土壤水分是控制陆地和大气间水热能量交换的关键因子之一,在地球生态系统中起着重要的作用。定量化获取土壤水分信息对农业生产、应对全球变化、保护生态环境等众多领域都有着重要意义。目前,获取精度较高的大区域土壤湿度信息依然是研究的热点和难点问题。气候变化倡议项目（climate change initiative,CCI）土壤湿度数据集是由多种主、被动微波数据融合的大尺度土壤湿度数据集,对其在中国区域的数据质量改进具有较高的实际应用价值。研究利用累积概率分布函数（cumulative distribution function,CDF）匹配方法对CCI土壤湿度产品进行改进。选择有较多实测数据的河北、山西、天津等部分区域,获得2009-2010年每月三旬（共72旬）的土壤湿度插值数据,以此为基础利用CDF进行重调整,建立逐像元的CCI土壤湿度数据的改进模型;然后利用站点实测数据进行该方法的有效性验证。结果表明,CDF调整前的偏差、均方根偏差和平均相对误差分别集中在0.05-0.09、0.05-0.1、0.20-0.45,调整后分别降低在0-0.04、0-0.05、0-0.2范围。可见,CDF调整后的误差明显减小,调整后的CCI土壤湿度的精度得到了明显的提高。
