The sea surface salinity(SSS) is a key parameter in monitoring ocean states. Observing SSS can promote the understanding of global water cycle. This paper provides a new approach for retrieving sea surface salinity fr...The sea surface salinity(SSS) is a key parameter in monitoring ocean states. Observing SSS can promote the understanding of global water cycle. This paper provides a new approach for retrieving sea surface salinity from Soil Moisture and Ocean Salinity(SMOS) satellite data. Based on the principal component regression(PCR) model, SSS can also be retrieved from the brightness temperature data of SMOS L2 measurements and Auxiliary data. 26 pair matchup data is used in model validation for the South China Sea(in the area of 4?–25?N, 105?–125?E). The RMSE value of PCR model retrieved SSS reaches 0.37 psu(practical salinity units) and the RMSE of SMOS SSS1 is 1.65 psu when compared with in-situ SSS. The corresponding Argo daily salinity data during April to June 2013 is also used in our validation with RMSE value 0.46 psu compared to 1.82 psu for daily averaged SMOS L2 products. This indicates that the PCR model is valid and may provide us with a good approach for retrieving SSS from SMOS satellite data.展开更多
To estimate sea surface temperature(SST)with high accuracy from radiometrie measure- ments,it is no longer acceptable to assume that sea surface emissivity is unity or any other con- stant.This note presents an invest...To estimate sea surface temperature(SST)with high accuracy from radiometrie measure- ments,it is no longer acceptable to assume that sea surface emissivity is unity or any other con- stant.This note presents an investigation of the desirable emissivity accuracy in relation to re- trieval.It was found that 1% error in surface emissivity can cause up to 0.7 K error in the re- trieved SST,although this sensitivity is often reduced to about 0.5 K on average because of the downward atmospheric radiation at surface partially compensates for the emissivity error.Since the downward atmospheric radiation ratio is controlled to a large extent by the integrated water vapor in the atmosphere and,secondarily,by view angle,the sensitivity of SST retrieval to surface emis- sivity has been computed as a function of these two parameters.展开更多
Several remotely sensed sea surface salinity（SSS） retrievals with various resolutions from the soil moisture and ocean salinity（SMOS） and Aquarius/SAC-D missions are applied as inputs for retrieving salinity profi...Several remotely sensed sea surface salinity（SSS） retrievals with various resolutions from the soil moisture and ocean salinity（SMOS） and Aquarius/SAC-D missions are applied as inputs for retrieving salinity profiles（S） using multilinear regressions. The performance is evaluated using a total root mean square（RMS） error, different error sources, and the feature resolutions of the retrieved S fields. In the mixed layer of the salinity, the SSS-S regression coefficients are uniformly large. The SSS inputs yield smaller RMS errors in the retrieved S with respect to Argo profiles as their spatial or temporal resolution decreases. The projected SSS errors are dominant, and the retrieved S values are more accurate than those of climatology in the tropics except for the tropical Atlantic, where the regression errors are abnormally large. Below that level, because of the influence of a sea level anomaly, the areas of high-accuracy S values shift to higher latitudes except in the high-latitude southern oceans, where the projected SSS errors are abnormally large. A spectral analysis suggests that the CATDS-0.25° results are much noisier and that the BEC-L4-0.25° results are much smoother than those of the other retrievals. Aquarius-CAP-1° generates the smallest RMS errors, and Aquarius-V2-1° performs well in depicting large-scale phenomena. BEC-L3-0.25°,which has small RMS errors and remarkable mesoscale energy, is the best fit for portraying mesoscale features in the SSS and retrieved S fields. The current priority for retrieving S is to improve the reliability of satellite SSS especially at middle and high latitudes, by developing advanced algorithms, combining both sensors, or weighing between accuracy and resolutions.展开更多
To retrieve sea-surface salinity (SSS) from radiometer data at 1.4 GHz, auxiliary data of sea-surface temperature (SST), surface roughness and meteorological variables are needed. The authors study oceanic passive pol...To retrieve sea-surface salinity (SSS) from radiometer data at 1.4 GHz, auxiliary data of sea-surface temperature (SST), surface roughness and meteorological variables are needed. The authors study oceanic passive polarimetric microwave remote sensing using 1.4 GHz and 10.7 GHz bands. A set of algorithms are developed for 1.4 GHz and 10.7 GHz microwave polarimetric radiometer at 50° incidence angle to retrieve wind vector, as well as other geophysical parameters, such as SSS, SST, atmospheric volumes of water vapor and liquid water. Idealized retrievals are conducted using 2 324 simulated brightness temperatures of full Stokes parameters at 1.4 GHz and 10.7 GHz. Results indicate that SSS, SST, sea-surface wind speed, direction, atmospheric volumes of water vapor and liquid water can be inversed at the same time. This suggests an alternative way for SSS remote sensing.展开更多
全球海洋盐度的分布和变化对于海洋生态系统和海洋气候系统都是非常重要的参数。盐度的被动微波辐射遥感虽然在L波段灵敏度最高,但是其在L波段的灵敏度低于其它许多变量,因此对盐度反演模型精度要求很高。通过介电常数模型、海表粗糙表...全球海洋盐度的分布和变化对于海洋生态系统和海洋气候系统都是非常重要的参数。盐度的被动微波辐射遥感虽然在L波段灵敏度最高,但是其在L波段的灵敏度低于其它许多变量,因此对盐度反演模型精度要求很高。通过介电常数模型、海表粗糙表面波谱模型和电磁散射模型的融合,并经过实验数据的比较,可以获得描述海表辐射率的理论模型表达式。概述了目前常见的L波段描述海表辐射率的理论模型和几种半经验模型,利用不同的组合模型Two-scale与Durden&Vesecky x 2结合、SSA与Elfouhaily结合和利用亮温依靠速度的Hollinger和Camps等人的半经验线性模型以及Gabarro等人的半经验模型,归纳了Gabarro等人利用WISE试验数据和EuroSTARRS数据反演盐度的结果,并依据盐度反演的质量对这些模型进行了评估。展开更多
基金supported by the National Natural Science Foundation of China under project 41275013the National High-Tech Research and development program of China under project 2013AA09A506-4the National Basic Research Program under project 2009CB723903
文摘The sea surface salinity(SSS) is a key parameter in monitoring ocean states. Observing SSS can promote the understanding of global water cycle. This paper provides a new approach for retrieving sea surface salinity from Soil Moisture and Ocean Salinity(SMOS) satellite data. Based on the principal component regression(PCR) model, SSS can also be retrieved from the brightness temperature data of SMOS L2 measurements and Auxiliary data. 26 pair matchup data is used in model validation for the South China Sea(in the area of 4?–25?N, 105?–125?E). The RMSE value of PCR model retrieved SSS reaches 0.37 psu(practical salinity units) and the RMSE of SMOS SSS1 is 1.65 psu when compared with in-situ SSS. The corresponding Argo daily salinity data during April to June 2013 is also used in our validation with RMSE value 0.46 psu compared to 1.82 psu for daily averaged SMOS L2 products. This indicates that the PCR model is valid and may provide us with a good approach for retrieving SSS from SMOS satellite data.
文摘To estimate sea surface temperature(SST)with high accuracy from radiometrie measure- ments,it is no longer acceptable to assume that sea surface emissivity is unity or any other con- stant.This note presents an investigation of the desirable emissivity accuracy in relation to re- trieval.It was found that 1% error in surface emissivity can cause up to 0.7 K error in the re- trieved SST,although this sensitivity is often reduced to about 0.5 K on average because of the downward atmospheric radiation at surface partially compensates for the emissivity error.Since the downward atmospheric radiation ratio is controlled to a large extent by the integrated water vapor in the atmosphere and,secondarily,by view angle,the sensitivity of SST retrieval to surface emis- sivity has been computed as a function of these two parameters.
基金The National Natural Science Foundation of China under contract No.41276088
文摘Several remotely sensed sea surface salinity（SSS） retrievals with various resolutions from the soil moisture and ocean salinity（SMOS） and Aquarius/SAC-D missions are applied as inputs for retrieving salinity profiles（S） using multilinear regressions. The performance is evaluated using a total root mean square（RMS） error, different error sources, and the feature resolutions of the retrieved S fields. In the mixed layer of the salinity, the SSS-S regression coefficients are uniformly large. The SSS inputs yield smaller RMS errors in the retrieved S with respect to Argo profiles as their spatial or temporal resolution decreases. The projected SSS errors are dominant, and the retrieved S values are more accurate than those of climatology in the tropics except for the tropical Atlantic, where the regression errors are abnormally large. Below that level, because of the influence of a sea level anomaly, the areas of high-accuracy S values shift to higher latitudes except in the high-latitude southern oceans, where the projected SSS errors are abnormally large. A spectral analysis suggests that the CATDS-0.25° results are much noisier and that the BEC-L4-0.25° results are much smoother than those of the other retrievals. Aquarius-CAP-1° generates the smallest RMS errors, and Aquarius-V2-1° performs well in depicting large-scale phenomena. BEC-L3-0.25°,which has small RMS errors and remarkable mesoscale energy, is the best fit for portraying mesoscale features in the SSS and retrieved S fields. The current priority for retrieving S is to improve the reliability of satellite SSS especially at middle and high latitudes, by developing advanced algorithms, combining both sensors, or weighing between accuracy and resolutions.
基金supported by Chinese Research Project under Grant No. 973-2007CB411807China Postdoctoral Science Foundation Funded Project No. 20070420070the Special Fund of China Postdoctoral Science Foundation
文摘To retrieve sea-surface salinity (SSS) from radiometer data at 1.4 GHz, auxiliary data of sea-surface temperature (SST), surface roughness and meteorological variables are needed. The authors study oceanic passive polarimetric microwave remote sensing using 1.4 GHz and 10.7 GHz bands. A set of algorithms are developed for 1.4 GHz and 10.7 GHz microwave polarimetric radiometer at 50° incidence angle to retrieve wind vector, as well as other geophysical parameters, such as SSS, SST, atmospheric volumes of water vapor and liquid water. Idealized retrievals are conducted using 2 324 simulated brightness temperatures of full Stokes parameters at 1.4 GHz and 10.7 GHz. Results indicate that SSS, SST, sea-surface wind speed, direction, atmospheric volumes of water vapor and liquid water can be inversed at the same time. This suggests an alternative way for SSS remote sensing.
文摘全球海洋盐度的分布和变化对于海洋生态系统和海洋气候系统都是非常重要的参数。盐度的被动微波辐射遥感虽然在L波段灵敏度最高,但是其在L波段的灵敏度低于其它许多变量,因此对盐度反演模型精度要求很高。通过介电常数模型、海表粗糙表面波谱模型和电磁散射模型的融合,并经过实验数据的比较,可以获得描述海表辐射率的理论模型表达式。概述了目前常见的L波段描述海表辐射率的理论模型和几种半经验模型,利用不同的组合模型Two-scale与Durden&Vesecky x 2结合、SSA与Elfouhaily结合和利用亮温依靠速度的Hollinger和Camps等人的半经验线性模型以及Gabarro等人的半经验模型,归纳了Gabarro等人利用WISE试验数据和EuroSTARRS数据反演盐度的结果,并依据盐度反演的质量对这些模型进行了评估。
