像元尺度土壤水分监测网络及其对L波段土壤水分产品的初步验证结果

土壤水分是作物生长、地—气水热交换及全球水循环过程中的关键变量,对于旱情监测、水文陆面过程及气候变化的研究具有重要的意义。被动微波遥感凭借对于土壤水分的敏感性已经成为监测土壤水分的主要手段。研究中针对吉林省农田下垫面,利用土壤水分传感器网络监测数据,开展了SMAP(Soil Moisture and Active and Passive)和SMOS(Soil Moisture and Ocean Salinity)被动微波土壤水分产品的真实性检验研究,得出了以下结论:(1)与实测数据相比较,SMOS L3(升降轨)和SMAP L3被动微波土壤水分产品存在低估现象,伴随降雨事件会出现高于实测土壤水分的情况;两种被动微波土壤水分产品的无偏均方根误差(unRMSE)都大于0.07m3/m3,但SMAP L3被动微波土壤水分产品数据的ubRMSE略低,为0.078m3/m3;(2)由于L波段的感应深度要浅于传感器的探测深度5cm,降雨后土壤表层的变干现象导致土壤水分的垂直不均匀性,这是SMOS和SMAP被动微波土壤水分产品低估土壤水分的原因之一;(3)SMOS与SMAP亮温分布范围对比结果表明:由于电磁射频干扰(RFI)的影响,RFI对于SMOS的影响更为严重,这或许是SMOS土壤水分产品的RMSE高于SMAP被动微波土壤水分产品的原因。     

反演策略对SMOS土壤水分反演算法的影响研究

为降低SMOS土壤水分反演算法的复杂度、提高土壤水分反演精度,对SMOS土壤水分反演策略进行调整：将多参数反演改为单参数反演以简化观测与模拟亮温的代价函数,以固定步长（0.001 m3/m3）代替不定步长从而避免复杂的矩阵运算,将围绕土壤水分先验值的少量局部搜索调整为全土壤水分区间（0~0.05 m³/m³）的密集全局搜索。利用美国USCRN 44个站点实测土壤水分分别与SMOS官方反演的土壤水分和SMOS调整算法反演的土壤水分进行对比分析。结果表明：与SMOS相比,算法调整后土壤水分的平均绝对偏差MAD、均方根误差RMSE和无偏均方根误差ubRMSE分别降低了0.012、0.018和0.020 m3/m3。     

基于SMOS、SMAP数据的青藏高原季风及植被生长季土壤水分长消特征研究

青藏高原地理位置特殊、环境特征显著,是地球系统作用的关键参与和决策者。利用大尺度的星载微波遥感数据开展其土壤水分研究,不仅能为理解典型地区对全球水、气、能、热交互机制的量化影响提供理论支持,还能够为证实遥感数据的可靠性提供实践依据。以SMOS(2011—2020)和SMAP(2016—2020)卫星土壤水分数据为主,以ISMN实测数据、GPCP降水数据、MOD16A2蒸散发数据、C3S地表类型数据为辅,利用土壤水分(年均值,■与时间之间的相关系数(R_xt),研究青藏高原土壤水分在季风及植被生长季(7—9月)的时空分布及长消特征;进而利用偏相关系数(R_xy,z),初步分析了土壤水分与降水和蒸散发的耦合关系。结果显示,青藏高原土壤水分在时间上呈现先减(2011—2015年)后增(2015—2018年)随后波动变化(2018—2020年)的趋势,在空间上呈现自西北向东南逐渐升高的趋势;大部分地区的土壤水分与降水的耦合表现强于蒸散发;SMOS和SMAP对青藏高原土壤水分时空特征的捕捉具有较高的一致性。     

基于多频被动微波遥感的土壤水分反演—以黑河上游为例

土壤水分是陆地生态系统中最重要的组成部分,如何有效地得到高精度的土壤水分产品成为当前研究最为关注的问题。被动微波遥感具有监测面积大、重访周期短、对土壤水分敏感等优点,成为反演土壤水分最有潜力的方式。基于SMOS（Soil Moisture and Ocean Salinity）和AMSR2（The Advanced Microwave Scanning Radiometer-2）数据,通过研究L波段与C波段融合亮度温度在土壤水分反演中的潜力,发展多频率土壤水分反演算法,并对黑河上游4个像元开展土壤水分反演研究。结果表明：①利用L/C组合亮温反演结果与实测数据较为吻合,长时间内变化趋势一致,相关系数为0.841,均方根误差为0.063 m³/m³。②通过与SMOS和AMSR2官方土壤水分产品比较发现,AMSR2土壤水分产品存在明显的低估,SMOS土壤水分产品缺失值较多,无法得到较为完整的土壤水分时间序列;利用L/C多频率组合反演得到的结果明显优于官方土壤水分产品。融合L与C波段亮温数据,可有效提高反演土壤水分精度,实现高精度土壤水分的获取。     

SMAP L2多尺度土壤水分数据与ISMN实测数据的差异特征研究

SMAP卫星的二级（L2）土壤水分数据是直接反演结果,能够从模型、算法、参数等多方面体现其对土壤水分反演的综合能力。在这一级别下,SMAP设计了包括L2＿SM＿P(36km)、L2＿SM＿P＿E(9 km)和L2＿SM＿SP(3 km和1 km)在内的多种尺度的土壤水分数据,能满足不同的实验和应用需求。以ISMN地面实测土壤水分数据作为对比参照,以偏差（Bias）、均方根误差（RMSE）、无偏均方根误差（ubRMSE）和相关系数（R）作为分析指标,分析了SMAP L2土壤水分数据和ISMN实测数据间的差异表现。结果显示：在不同静态条件下（气候类型、土壤性质和植被类型）,植被对差异的影响最大,土壤性质的影响最小;在不同动态条件下（土壤水分、植被光学厚度和地表温度）,植被光学厚度和土壤水分对差异影响较大,地表温度的影响较小;在4种SMAP L2土壤水分数据中,9 km数据与ISMN实测数据的差异最小,其次是36、3、1 km尺度的数据;结合静态条件和动态条件来看,36 km和9 km尺度的数据与ISMN实测数据的差异情况类似,3 km和1 km数据差异情况类似。     

基于SMAP亮温数据反演青藏高原玛曲区域土壤未冻水

未冻水和冰共同存在于冻土中,两者的相互转化即冻融变化深刻影响寒区地表水分循环和能量收支。被动微波遥感技术是土壤水分监测的主要手段,但目前大多应用于非冻结土壤的水分反演,对负温环境下冻结土壤中未冻水的反演研究较少。基于SMAP卫星升轨和降轨时刻的亮温观测数据和经改进后适用于青藏高原地区的零阶微波辐射模型,利用单通道算法（SCA）和双通道算法（DCA）,对青藏高原东部黄河源区玛曲区域季节冻土中的未冻水含量进行反演。结果表明：基于SMAP不同过境时刻亮温观测及不同算法的土壤未冻水反演结果均较同步地反映了研究区实测值的动态变化特征（相关系数R均大于0.9）。其中,基于SMAP降轨时刻亮温观测的反演结果在冻融交替的过渡季节存在明显低估,而基于升轨时刻亮温观测得到的反演结果精度更高。基于垂直极化亮温观测的单通道（SCA-V）和DCA算法得到的升轨时刻的反演值与实测值的无偏均方根误差（ubRMSE）分别为0.035 m³m^-3和0.039 m³m^-3,均达到SMAP任务的设计要求（即ubRMSE≤0.04 m³m^-3）,其中SCA-V对该研究区土壤未冻水的反演精度最高。与SMAP标准产品相比,基于SCA-V算法反演得到的暖季土壤水分精度更高。此外,该算法能成功反演得到冻结期土壤未冻水的动态变化,因此更适用于青藏高原地区冻融土壤条件下的水分反演。     

基于Triple-Collocation方法的微波遥感土壤水分产品不确定性分析及数据融合

微波遥感可以获取大范围的地表土壤水分信息，以及由此得到全球尺度的土壤水分产品。但由于传感器观测配置和反演方法等诸多因素的影响，使得不同的土壤水分产品在精度和可靠性方面存在差异。基于Triple-Collocation（TC）方法，在青藏高原那曲地区的0.25°×0.25°和1.0°×1.0°两个空间尺度上对AMSR2、SMAP和SMOS 3种土壤水分遥感产品进行不确定性分析，开展基于随机误差的数据融合算法研究。研究结果表明:不同遥感产品间的随机误差在空间分布上存在显著的不一致性，使得应用传统的算术平均方法进行数据融合不具有普适性。基于此不确定性，对3种产品配赋相应的权重进行融合，相比于3种土壤水分原始数据集，融合产品不仅具有更丰富的数据量，也会对数据精度有所改善。当遥感产品间的随机误差接近时，等权重和优化权重的融合结果非常接近；当遥感产品间的随机误差差异较大时，基于不确定性的数据融合方法相比等权重方法可以明显的提高融合数据的精度。     

基于Triple-Collocation方法的微波遥感土壤水分产品不确定性分析及数据融合

微波遥感可以获取大范围的地表土壤水分信息,以及由此得到全球尺度的土壤水分产品。但由于传感器观测配置和反演方法等诸多因素的影响,使得不同的土壤水分产品在精度和可靠性方面存在差异。基于Triple-Collocation（TC）方法,在青藏高原那曲地区的0.25°×0.25°和1.0°×1.0°两个空间尺度上对AMSR2、SMAP和SMOS 3种土壤水分遥感产品进行不确定性分析,开展基于随机误差的数据融合算法研究。研究结果表明:不同遥感产品间的随机误差在空间分布上存在显著的不一致性,使得应用传统的算术平均方法进行数据融合不具有普适性。基于此不确定性,对3种产品配赋相应的权重进行融合,相比于3种土壤水分原始数据集,融合产品不仅具有更丰富的数据量,也会对数据精度有所改善。当遥感产品间的随机误差接近时,等权重和优化权重的融合结果非常接近;当遥感产品间的随机误差差异较大时,基于不确定性的数据融合方法相比等权重方法可以明显的提高融合数据的精度。     

祁连山区不同植被类型上的SMOS遥感土壤水分产品质量评估

为了分析SMOS遥感土壤水分产品在祁连山区的真实性和可靠性,利用祁连山区内布设于7种主要植被类型上的34个实测站点的实测土壤水分数据对其进行质量评估。首先挑选与实测值相对应的SMOS数据,进而依次计算每个站点上遥感产品与实测值的相关系数R、Bias和均方根误差RMSE,从而得到SMOS数据在不同植被类型上不同尺度(年和季节)的反演精度。结果表明:SMOS遥感土壤水分产品在研究区内是可信的,但低估了研究区土壤水分值,且未能达到产品预期目标0.04m~3/m~3。SMOS产品对于植被辐射反演效果好于土壤辐射反演,导致其在植被覆盖度越高的区域与实测值的拟合程度越高。SMOS产品在湿润条件下性能优于干旱条件,在变异性小的地区性能优于变异性大的地区。在季节尺度上,SMOS遥感产品与实测值拟合程度在夏、秋两季远好于春季。     

机载L波段微波辐射计数据反演表层土壤水分研究

土壤水分是联系地球表层物质能量交换的重要纽带,准确监测土壤水分对区域气候、生态、水文及农业生产研究意义重大。机载L波段微波辐射计提供了获取区域土壤水分"真值"的有效手段。结合黑河中游航空试验中的多源遥感及地面观测,发展了一种基于0°入射角的L波段被动微波亮温数据的单通道土壤水分反演方法,获得了研究区3景约700m空间分辨率的土壤水分反演结果。并对其反演结果进行了点尺度、面尺度和村社尺度3种不同空间尺度上的验证,结果显示:L波段被动微波遥感反演土壤水分在点尺度上的验证精度在0.035~0.055m3/m3之间;面尺度上验证精度略高于点尺度,其验证偏差在0.02m3/m3以内;反演土壤水分与村社尺度的灌溉数据,即距前次灌溉的间隔日数,在空间上负相关关系明显,二者间相关系数约为0.3。     

荷兰农业水土资源利用研究与遥感技术的应用-赴荷兰访问考察团

根据中荷两国学者互访协议,中国科学院沙漠所派我们两人在1985年10月10日至11月6日对荷兰进行了为期四周的考察访问。在荷期间,我们受到荷方学者热情友好的接待,首后访问了国际农业中心(IAC—     

Pixel-scalesoil Moisture Monitoring Network and Its Preliminary Validation of L-band Soil Moisture Products

Soil moisture is a key variable in the process of crop growth,ground-air water heat exchange and global water cycle,which plays an important role in drought monitoring,hydrological land surface processes and climate change.Passive microwave remote sensing has become the main means of monitoring soil moisture with the sensitivity to soil moisture.In this study,the authenticity test of SMAP（Soil Moisture and Active and Passive） and SMOS（Soil Moisture and Ocean Salinity）passive microwave soil moisture products using the soil moisture sensor network monitoring data carried out against the underlying surface of farmlands in Jilin Province was carried out.The following conclusions were obtained：（1）Compared with the in situ measured data,SMOS L3(ascending and descending overpasses) and SMAP L3 passive microwave soil moisture products generally underestimated the ground data,but With the occurrence of rainfall events,there will be the phenomenon which is the value of soil moisture products is higher than the in situ data; although the unbiased root mean square error (unRMSE) of the two soil moisture products was greater than 0.07 m3/m3,the unRMSE of SMAP passive microwave soil moisture product data which was 0.078 m3/m3 was slightly lower;（2）Since the depth of induction of the L-band is lighter than the depth of detection of the sensor(5cm),and the dryness of the soil surface after rainfall causes the vertical inhomogeneity of soil moisture,which is one of the reasons why SMOS and SMAP passive microwave soil moisture products underestimate soil moisture; （3）SMOS has a higher value than the range of SMAP brightness temperature,which may be caused by radio frequency interference (RFI),which makes the error of soil moisture Retrieval and affects the validation accuracy.The comparison of bright temperature distribution of SMOS and SMAP shows that the effect of RFI on SMOS is more serious due to the influence of electromagnetic radio frequency interference (RFI),which may be the reason why the RMSE of soil moisture product of SMOS is higher than that of passive microwave soil moisture product of SMAP.     

Impact of surface heterogeneity on surface soil moisture retrievals from passive microwave data at the regional scale: The Upper Danube case

Water and energy fluxes at the interface between the land surface and atmosphere are strongly depending on the surface soil moisture content which is highly variable in space and time. The sensitivity of active and passive microwave remote sensing data to surface soil moisture content has been investigated in numerous studies. Recent satellite borne mission concepts, as e.g. the SMOS mission, are dedicated to provide global soil moisture information with a temporal frequency of 1-3 days to capture it's high temporal dynamics. Passive satellite microwave sensors have spatial resolutions in the order of tens of kilometres. The retrieved soil moisture fields from that sensors therefore represent surface information which is integrated over large areas. It has been shown that the heterogeneity within an image pixel might have considerable impact on the accuracy of soil moisture retrievals from passive microwave data.The paper investigates the impact of land surface heterogeneity on soil moisture retrievals from L-band passive microwave data at different spatial scales between 1 km and 40 km. The impact of sensor noise and quality of ancillary information is explicitly considered. A synthetic study is conducted where brightness temperature observations are generated using simulated land surface conditions. Soil moisture information is retrieved from these simulated observations using an iterative approach based on multiangular observations of brightness temperature. The soil moisture retrieval uncertainties resulting from the heterogeneity within the image pixels as well as the uncertainties in the a priori knowledge of surface temperature data and due to sensor noise, is investigated at different spatial scales. The investigations are made for a heterogeneous hydrological catchment in Southern Germany (Upper Danube) which is dedicated to serve as a calibration and validation site for the SMOS mission.     

国地球资源遥感卫星的工作状况和研制动态

In order to reduce the complexity of SMOS official soil moisture retrieval algorithm and improve the accuracy of soil moisture retrievals, a new retrieval strategy on SMOS soil moisture retrieval algorithm was developed. In the new retrieval strategy on SMOS soil moisture retrieval algorithm, the fixed step size (0.001 m3/m3) was used to replace the flexible step size obtained by the SMOS matrix operation. The multi-parameter was changed to a single-parameter in the cost function. The data from 44 USCRN sites in the United States were compared with the soil moisture retrieved from SMOS official algorithm as well as the adjustment of SMOS algorithm. The results show that compared with the SMOS official algorithm, the average absolute deviation, root mean square error,and unbiased root mean square error of the adjustment of SMOS algorithm are reduced by 0.012 m3/m3, 0.018 m3/m3,and 0.020 m3/m3,respectively.     

Validation of SMOS Soil Moisture Products on Different Vegetation Types in Qilian Mountain

Observation data of 34 in-situ stations located in seven main vegetation types were used to evaluate the performance of SMOS soil moisture products in Qilian Mountain,Northwest China.SMOS data were processed to correspond to the observation data,and three indices：R、Biasand RMSE were calculated at both annual and seasonal scales for each observation station.Results show that SMOS products were credible in the study area,but underestimated soil moisture in Qilian Mountain,and failed to achieve the intended accuracy target of 0.04 m3/m3.SMOS performed better in estimating vegetation emission than soil emission,leading to its better performance in areas with higher vegetation coverage.Similarly,SMOS performed better in the humid condition than the arid condition,and also better in areas with smaller soil moisture variability than those with large soil moisture variability.At seasonal scale,SMOS products fitted the observations better in the summer and autumn than the spring.     

Error Characteristics of Microwave Soil Moisture Products based on Triple Collocation and Its Spatial-temporal Pattern

Soil moisture is an important state variable connecting the land surface-atmosphere system, and its information can be efficiently acquired by the new technique of microwave remote sensing. Accurate interpretation of the microwave soil moisture products qualities and in-depth understanding of their temporal and spatial distributions are important prerequisites for their successful application in earth science through data assimilation. In this study, three microwave soil moisture products, FengYun-3C(FY-3C), Soil Moisture Active Passive (SMAP) and Advanced Scatterometer(ASCAT), were evaluated over China based on the triple collocation (TC) method. The abilities of three products to obtain temporal and spatial variations of soil moisture were illustrated by Hovm?ller diagram. The results show that: (1) SMAP generally outperforms ASCAT and FY-3C, with highest TC-based signal-to-noise ratio(SNR) under different land use types. The TC-based SNRs are 1.668dB, -0.316dB and -2.182dB for SMAP, ASCAT and FY-3C respectively; and their correlation coefficients with ground observations are 0.514, 0.501 and 0.209, respectively. (2) The accuracies of FY-3C and ASCAT in Northwest China are overall higher than those in the southern China. All three products can capture the latitudinal and longitudinal gradients of soil moisture, whereas their seasonal fluctuations are higher than those of in-situ measurements. Among three products, FY-3C shows highest spatial gradient and strongest seasonal fluctuations. (3) FY-3C product performance is more susceptible to vegetation coverage than ASCAT and SMAP, but it outperforms ASCAT in barren areas. The results of our study could provide useful insights for assimilating microwave soil moisture products into land surface models to improve hydrological prediction.