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.

An analysis on the error structure and mechanism of soil moisture and ocean salinity remotely sensed sea surface salinity products

For the application of soil moisture and ocean salinity（SMOS） remotely sensed sea surface salinity（SSS） products,SMOS SSS global maps and error characteristics have been investigated based on quality control information.The results show that the errors of SMOS SSS products are distributed zonally,i.e.,relatively small in the tropical oceans,but much greater in the southern oceans in the Southern Hemisphere（negative bias） and along the southern,northern and some other oceanic margins（positive or negative bias）.The physical elements responsible for these errors include wind,temperature,and coastal terrain and so on.Errors in the southern oceans are due to the bias in an SSS retrieval algorithm caused by the coexisting high wind speed and low temperature; errors along the oceanic margins are due to the bias in a brightness temperature（TB） reconstruction caused by the high contrast between L-band emissivities from ice or land and from ocean; in addition,some other systematic errors are due to the bias in TB observation caused by a radio frequency interference and a radiometer receivers drift,etc.The findings will contribute to the scientific correction and appropriate application of the SMOS SSS products.

Validation and application of soil moisture active passive sea surface salinity observation over the Changjiang River Estuary

Using sea surface salinity(SSS)observation from the soil moisture active passive(SMAP)mission,we analyzed the spatial distribution and seasonal variation of SSS around Changjiang River(Yangtze River)Estuary for the period of September 2015 to August 2018.First,we found that the SSS from SMAP is more accurate than soil moisture and ocean salinity(SMOS)mission observation when comparing with the in situ observations.Then,the SSS signature of the Changjiang River freshwater was analyzed using SMAP data and the river discharge data from the Datong hydrological station.The results show that the SSS around the Changjiang River Estuary is significantly lower than that of the open ocean,and shows significant seasonal variation.The minimum value of SSS appears in July and maximum SSS in December.The root mean square difference of daily SSS between SMAP observation and in situ observation is around 3 in both summer and winter,which is much lower than the annual range of SSS variation.In summer,the diffusion direction of the Changjiang River freshwater depicted by SSS from SMAP is consistent with the path of freshwater from in situ observation,suggesting that SMAP observation may be used in coastal seas in monitoring the diffusion and advection of freshwater discharge.

Using multi-satellite microwave remote sensing observations for retrieval of daily surface soil moisture across China

The objective of this study was to retrieve daily composite soil moisture by jointly using brightness temperature observations from multiple operating satellites for near real-time application with better coverage and higher accuracy.Our approach was to first apply the single-channel brightness radiometric algorithm to estimate soil moisture from the respective brightness temperature observations of the SMAP,SMOS,AMSR2,FY3B,and FY3C satellites on the same day and then produce a daily composite dataset by averaging the individual satellite-retrieved soil moisture.We further evaluated our product,the official soil moisture products of the five satellites,and the ensemble mean (i.e.,arithmetic mean) of the five official satellite soil moisture products against ground observations from two networks in Central Tibet and Anhui Province,China.The results show that our product outperforms the individual released products of the five satellites and their ensemble means in the two validation areas.The root mean square error (RMSE ) values of our product were 0.06 and 0.09 m3/m3 in Central Tibet and Anhui Province,respectively.Relative to the ensemble mean of the five satellite products,our product improves the accuracy by 9.1% and 57.7% in Central Tibet and Anhui Province,respectively.This demonstrates that jointly using brightness temperature observations from multiple satellites to retrieve soil moisture not only improves the spatial coverage of daily observations but also produces better daily composite products.

A new method to retrieve salinity profiles from sea surface salinity observed by SMOS satellite

This paper proposes a new method to retrieve salinity profiles from the sea surface salinity （SSS） observed by the Soil Moisture and Ocean Salinity （SMOS） satellite. The main vertical patterns of the salinity profiles are firstly extracted from the salinity profiles measured by Argo using the empirical orthogonal function. To determine the time coefficients for each vertical pattern, two statistical models are developed. In the linear model, a transfer function is proposed to relate the SSS observed by SMOS （SMOS_SSS） with that measured by Argo, and then a linear relationship between the SMOS_SSS and the time coefficient is established. In the nonlinear model, the neural network is utilized to estimate the time coefficients from SMOS_SSS, months and positions of the salinity profiles. The two models are validated by comparing the salinity profiles retrieved from SMOS with those measured by Argo and the climatological salinities. The root-mean-square error （RMSE） of the linear and nonlinear model are 0.08-0.16 and 0.08-0.14 for the upper 400 m, which are 0.01-0.07 and 0.01-0.09 smaller than the RMSE of climatology. The error sources of the method are also discussed.

Preliminary validation of SMOS sea surface salinity measurements in the South China Sea

The SMOS(soil moisture and ocean salinity) mission undertaken by the European Space Agency(ESA) has provided sea surface salinity(SSS) measurements at global scale since 2009.Validation of SSS values retrieved from SMOS data has been done globally and regionally.However,the accuracy of SSS measurements by SMOS in the China seas has not been examined in detail.In this study,we compared retrieved SSS values from SMOS data with in situ measurements from a South China Sea(SCS) expedition during autumn 2011.The comparison shows that the retrieved SSS values using ascending pass data have much better agreement with in situ measurements than the result derived from descending pass data.Accuracy in terms of bias and root mean square error(RMS) of the SSS retrieved using three different sea surface roughness models is very consistent,regardless of ascending or descending orbits.When ascending and descending measurements are combined for comparison,the retrieved SSS using a semi-empirical model shows the best agreement with in situ measurements,with bias-0.33 practical salinity units and RMS 0.74.We also investigated the impact of environmental conditions of sea surface wind and sea surface temperature on accuracy of the retrieved SSS.The SCS is a semi-closed basin where radio frequencies transmitted from the mainland strongly interfere with SMOS measurements.Therefore,accuracy of retrieved SSS shows a relationship with distance between the validation sites and land.

Validation and correction of sea surface salinity retrieval from SMAP

In this study, sea surface salinity(SSS) Level 3(L3) daily product derived from soil moisture active passive(SMAP)during the year 2016, was validated and compared with SSS daily products derived from soil Moisture and ocean salinity(SMOS) and in-situ measurements. Generally, the root mean square error(RMSE) of the daily SSS products is larger along the coastal areas and at high latitudes and is smaller in the tropical regions and open oceans. Comparisons between the two types of daily satellite SSS product revealed that the RMSE was higher in the daily SMOS product than in the SMAP, whereas the bias of the daily SMOS was observed to be less than that of the SMAP when compared with Argo floats data. In addition, the latitude-dependent bias and RMSE of the SMAP SSS were found to be primarily influenced by the precipitation and the sea surface temperature(SST). Then, a regression analysis method which has adopted the precipitation and SST data was used to correct the larger bias of the daily SMAP product. It was confirmed that the corrected daily SMAP product could be used for assimilation in high-resolution forecast models, due to the fact that it was demonstrated to be unbiased and much closer to the in-situ measurements than the original uncorrected SMAP product.

A method for correcting regional bias in SMOS global salinity products

Soil Moisture and Ocean Salinity （SMOS） Level 3 （L3） sea surface salinity （SSS） products are provided by the Barcelona Expert Centre （BEC）. Strong biases were observed on the SMOS SSS products, thus the data from the Centre Aval de Traitement des Donnees SMOS （CATDS） were adjusted for biases using a large-scale correction derived from observed differences between the SMOS SSS and World Ocean Atlas （WOA） climatology data. However, this large-scale correction method is not suitable for correcting the large gradient of salinity biases. Here, we present a method for the correction of SSS regional bias of the monthly L3 products. Based on the stable characteristics of the large SSS biases from month to month in some regions, corrected SMOS SSS maps can be obtained from the monthly mean values after removing the regional biases. The accuracy of the SMOS SSS measurements is greatly improved, especially near the coastline, at high latitudes, and in some open ocean regions. The SMOS and ISAS SSS data are also compared with Aquarius SSS to verify the corrected SMOS SSS data. The correction method presented here only corrects annual mean biases. The measurement accuracy of the SSS may be improved by considering the influence of atmospheric and ocean circulation in different seasons and years.

A performance evaluation of remotely sensed sea surface salinity products in combination with other surface measurements in reconstructing three-dimensional salinity fields

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.

基于SMOS数据的三江源土壤水分时空变化

基于SMOS卫星和GNSS-MET地面土壤水分监测数据,采用皮尔逊相关、滑动平均、反距离权重法等方法,评估SMOS土壤水分数据在三江源地区的适用性,分析土壤水分的变化特征及其影响因素。结果表明:(1)SMOS土壤水分数据能反应研究区(4月-10月)基本规律,其值略低于地面实测值;(2)SMOS卫星升轨土壤水分值大于降轨土壤水分值,6月-8月SMOS土壤水分值高于其它月份;土壤水分季节性差异明显,夏季显著高于春季;(3)三江源区土壤水分空间分布差异较大,空间格局明显,总体上澜沧江源区相对湿润,长江源区相对干旱。

赤道太平洋SMOS海表盐度数据的评估及借助神经网络的订正

文章对土壤湿度和海洋盐度（soil moisture and ocean salinity, SMOS）卫星遥感所得2011-2012 年赤道太平洋海域海表盐度数据进行了质量控制并首次分析了盐度反演误差的海洋动力过程影响因子, 在此基础上引入神经网络方法对同时期的盐度数据进行了订正.研究发现, 降水及其诱发的表面波会使盐度误差向负方向显著增长; 海面风场导致的海面粗糙度会增大盐度误差, 风速与盐度误差呈微弱正相关; 海表温度变化则对盐度反演无影响.考虑降雨、风速等主要海洋动力过程影响因子, 利用神经网络方法对2011 年12 月赤道太平洋海域的海表盐度数据进行了订正, 其均方根误差由0.3837 降到0.2441.结果发现, 订正后的盐度数据不但消除了因降水等动力过程导致的盐度误差, 亦在赤道太平洋海域揭示了原SMOS 数据无法刻画的高盐舌现象.

SMOS与Aquarius卫星海表盐度测量方法及数据的对比分析

国际上发射的海表盐度遥感卫星主要有2颗:欧洲的SMOS和美国的Aquarius卫星,为给后续海表盐度遥感提供参考借鉴,对比分析了这2颗卫星的遥感器载荷、数据处理算法和盐度数据。遥感器载荷方面,SMOS采用L波段二维综合孔径辐射计,而Aquarius采用L波段实孔径辐射计加散射计;数据处理算法方面,分析了二者在介电常数模型、海面粗糙度校正以及反演算法方面的差异;盐度数据方面,分析了SMOS与Aquarius盐度数据之间的相关程度,并分别与ISAS(In Situ Analysis System)浮标盐度数据作对比,分析了2颗卫星的盐度数据精度。将2颗卫星的盐度遥感数据与ISAS浮标盐度数据对比发现,在全球范围内,Aquarius盐度测量精度优于SMOS;但在开阔海域,SMOS盐度测量精度优于Aquarius;而在近海岸区域,均出现较大的误差,且SMOS数据误差更大。

国外海洋盐度与土壤湿度探测卫星的发展

文章调查研究了国外海洋盐度与土壤湿度探测卫星发展情况、探测机理;重点对SMOS、Aquarius、SMAP等三颗最具代表性的土壤湿度和海洋盐度探测卫星进行了研究分析,比对了三颗卫星的主要技术指标,并总结了其技术发展特点和关键技术;最后结合我国技术基础对发展我国盐度与湿度卫星提出了建议。

海洋盐度卫星农业遥感应用研究进展

海洋盐度卫星具有在海洋渔业和耕地土壤水分等领域开展全球遥感监测应用的潜力。笔者从盐度卫星发展现状、海洋渔业和耕地土壤水分遥感应用3个方面出发,对以往盐度卫星农业遥感监测与反演研究进展进行了概述,并对其农业遥感监测应用前景进行了展望。通过总结认为:在海洋渔业遥感研究领域,盐度卫星应用仍然是以温度、盐度等反演算法研究为主,这些基础性的研究为海洋渔业遥感监测应用提供了必要的因子;在土壤湿度研究领域,盐度卫星的应用相对成熟,基于不同地表覆盖条件下精度分析,开发的适用于土壤湿度产品生产的反演算法,是耕地土壤墒情遥感监测的基础。在总结当前海洋盐度卫星农业遥感应用进展的基础上,展望了其发展方向,并可为中国新一代海洋盐度卫星的研制提供农业应用依据。

Sentinel-2多光谱卫星遥感反演植被覆盖下的土壤盐分变化

为克服植被覆盖条件下土壤盐分含量与光谱反射率之间相关性较差所带来反演精度较低的问题,该研究以内蒙古河套灌区沙壕渠灌域为研究区域,利用Sentinel-2卫星同步获取光谱数据,通过构建以归一化植被指数(Normalized Difference Vegetation Index,NDVI)为分支标准的盐分深度决策树确定反演土壤盐分含量的最佳深度,然后构建以NDVI和表层土壤含水率为分支标准的类别决策树,将土壤样本划分为不同类别,以此分别构建土壤盐分反演模型,并评估反演效果。研究结果表明,决策树能增强光谱反射率对土壤盐分含量的敏感性,基于随机森林(Random Forest,RF)的盐分反演模型可取得理想的反演效果,决定系数为0.70,均方根误差为0.25%,相对分布误差为0.35,相对分析误差为1.67。土壤盐分含量反演模型能较好地反演表层(<20 cm)和深层(>40~60 cm)土壤盐分含量,在反演中层(20~40 cm)土壤盐分含量上存在一定局限。当地表有植被覆盖时,利用决策树可有效地提高土壤盐分含量的反演精度(与未考虑决策树相比,决定系数和相对分析误差分别提高0.32和0.80)。研究结果可为监测灌区内作物生育期间土壤盐分含量的动态变化提供方法参考。

海表面盐度的高精度预测模型

为了建立高精度的海洋表面盐度预测模型,采用BP神经网络的方法,针对SMOS卫星level 1C级亮度温度数据和辅助数据建立了一种海表面盐度预测模型,以ARGO浮标观测值作为海表盐度实测值来检验新模型预测结果的准确度,同时利用验证集对模型的精度进行验证。结果表明:通过新模型预测的海表盐度(SSS0)比SMOS卫星的3个粗糙度模型盐度产品(SSS1,SSS2,SSS3)精度高;SSS0,SSS1,SSS2,SSS3与ARGO浮标实测盐度(SSS ARGO)的均方根误差分别为0.8473,2.0417,2.0288和2.0805,平均绝对误差分别为0.7553,1.4226,1.4216和1.4566,SSS0与SSS ARGO的均方根误差和绝对平均误差值都明显小于SSS1,SSS2和SSS3与SSS ARGO的;由此可见,建立的海表盐度预测模型精度较高。新模型为海表盐度的反演算法提供了新思路。

新型遥感卫星土壤水分产品在不同地表覆盖下的精度验证与分析

近年来,土壤水分遥感卫星数据被广泛应用在气候、水文、农业等领域的研究中,微波土壤水分数据产品的适用性分析显得越来越重要。本研究分别以美国国家航空航天局的FLUXNET2015数据集和欧洲航天局发起并进行集中数据托管的ISMN网络为基准,运用统计学的方法(偏差、均方根误差和时间序列相关系数),在2012年7月至2014年12月,以IGBP(国际地圈生物圈计划)为标准的地表覆盖类型进行分类,在全球范围内的FLUXNET站点对SMOS-L3-SM和AMSR2-LPRM-SM两种遥感土壤水分产品与实测数据进行了对比验证,并在2016年1月至2017年12月,以30 m全球地表覆盖产品(GlobeLand30)为标准的地表覆盖类型进行分类,在全球范围内的ISMN站点对SMAP-L3-SM和AMSR2-LPRM-SM两种遥感土壤水分产品与实测数据进行了对比验证。结果表明:三种遥感土壤水分产品在草地等低矮植被类型低矮稀疏植被覆盖区域的土壤水分产品反演精度均要高于高大茂密植被覆盖的区域,且SMOS和AMSR2产品在地表覆盖为针叶林下反演精度较低,SMAP和AMSR2产品在地表覆盖为湿地下反演精度较低,SMAP产品的反演精度要高于AMSR2产品。

利用星载GNSS-R DDM反演土壤湿度可行性分析

针对目前全球卫星导航系统星载反射信号(GNSS-R)土壤湿度探测技术主要停留在地基以及机载观测研究,难以实现星载大范围探测这一难题,提出利用星载GNSS-R时延多普勒图(DDM)数据进行土壤湿度反演:首先建立了DDM信噪比(SNR)与土壤湿度数据相关性模型,然后利用英国技术演示卫星(UK TDS-1)的DDM以及欧洲航天局土壤湿度与海水盐度(SMOS)卫星的土壤湿度数据对模型进行了验证。结果表明,DDM SNR与土壤湿度数据具有较强的相关性:2者在植被覆盖度较高以及接近裸土的2块区域均呈现出较高的相关系数,说明利用星载GNSS-R DDM反演土壤湿度具有一定的可行性。

随机森林反演卫星遥感海表面盐度研究

海表面盐度是描述海洋状态、模拟海洋循环和检测气候变化的重要指标,对海洋研究意义重大。土壤湿度与海水盐度(soil moisture and ocean salinity,SMOS)卫星为全球海表面盐度分析提供了重要数据,但其整体精度尚未达到预期要求。基于海表面盐度遥感机理和SMOS卫星盐度反演基础理论,选取海表面盐度敏感因子,建立随机森林(random forest,RF)模型,并基于网格搜索算法优化模型参数,辅助提高SMOS卫星产品精度。其中基础RF得到的海表面盐度与Argo(array for real-time geostrophic oceanography)数据之间的平均绝对误差为0.08,均方根误差为0.15。而经网格搜索算法优化后的随机森林模型精度稍有所提升,其与Argo数据的绝对平均误差为0.08,均方根误差仅为0.14,且误差分布范围较小。两种模型均显著优于SMOS卫星Level 2级盐度产品。从机器学习与统计学理论出发,建立的高精度、高适应性的随机森林海表面盐度反演模型大幅提高了盐度精度,能够为相关海洋研究提供数据支撑。