基于Triple Collocation方法的微波土壤水分产品不确定性分析与时空变化规律研究

土壤水分是连接地—气系统的重要状态变量,微波遥感为准确获取大面积土壤水分信息提供新的技术手段。准确解读微波土壤水分产品质量、深入了解其误差的时空分布特征是通过数据同化等方法将其融入陆面模型,从而成功应用于地球科学领域的重要先决条件。基于Triple Collocation(TC)方法检验了风云三号C星(FY-3C)、土壤水分主被动卫星(SMAP)及高级微波散射计(ASCAT)这3种常用微波土壤水分产品在中国陆域的质量,并通过Hovm?ller图评估了3套产品捕捉土壤水分时空变化的能力。结果显示:①TC方法得到的分析结论与地面实测资料的验证结果一致,整体上SMAP优于ASCAT和FY-3C,不同土地利用类型下SMAP信噪比均最高,三者的TC信噪比分别为1.668 dB、-0.316 dB和-2.182 dB,同时三者与实测值的相关系数分别为0.514、0.501和0.209;②FY-3C和ASCAT产品的精度在中国西北地区整体优于南部地区,3种产品均能较好地刻画土壤水分随纬度和经度变化的情况,3种产品展现的季节波动整体高于实测,其中FY-3C的季节波动在3种产品中最为剧烈;③FY-3C的质量比ASCAT和SMAP更易受到植被影响,但在裸土区FY-3C优于ASCAT。本研究基于TC分析提供了全国范围内3种主流微波土壤水分产品的误差和信噪比的空间分布,并通过Hovm?ller图评估了其描述土壤水分时空变化的能力。研究结论可为微波土壤水分产品的同化研究提供一定参考。     

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.     

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—     

SMOS与SMAP过境时段表层土壤水分的稳定性研究

SMOS和SMAP都是为获取全球土壤水分信息而设计的专题卫星,均搭载了L波段辐射计。进行二者的横向对比是构建具有一致性的全球土壤水分数据集的关键基础。虽然SMAP、SMOS名义上的过境时刻是固定的,但二者的实际过境时刻随时间和空间发生变化,它们与地面实测数据三者之间难以匹配形成时序上严格统一的样本对,从而给土壤水分反演结果的精度评定带来困难。针对这一问题,以美国大陆地区为研究区,首先对2016~2017年SMOS、SMAP土壤水分数据的时间戳进行统计,判定二者过境的交叠时段;进而利用高观测频率、大空间尺度的实测数据,研究表层土壤水分在此时段内的自然变化特征。结果显示,按照全部、无降水、有降水3种条件,在样本量分别为98.14%、99.51%和88.49%的绝大多数情况下,表层土壤水分的变化量为0.007 m3/m3、0.007 m3/m3和0.012 m3/m3, 远小于SMOS、SMAP的目标精度（0.04 m3/m3）。初步证实： ①SMOS与SMAP的土壤水分反演结果（L2数据）可进行直接比对;②过境时刻差异对验证误差的影响可不计。     

青藏高原L波段微波辐射观测与土壤水分反演研究进展

土壤水分是地气间水热交换的重要变量,影响着地表感热潜热划分、水分收支和植被蒸腾等过程,青藏高原土壤水分的研究对于改进高原水分循环和能量平衡的模拟研究具有重要意义。随着SMOS、SMAP等卫星的发射,L波段被动微波遥感技术成为大尺度监测土壤水分的主要手段。分别从L波段星—机—地观测与微波辐射模拟、区域尺度土壤水分观测、卫星产品评估与土壤水分反演算法发展等方面系统回顾和总结了近年来L波段被动微波遥感及其土壤水分反演算法、产品在青藏高原的主要应用与研究进展。在此基础上,归纳了当前高原L波段被动微波辐射模拟与土壤水分反演存在的问题,主要包括缺乏高原尺度的微波辐射模拟评估和改进的卫星土壤水分产品、土壤冻结时期的水分监测产品依然缺失等问题。针对存在的问题,进一步提出了相关建议与展望,建议今后的研究应加强高原尺度的微波辐射模拟评估与土壤水分产品改进工作,并积极拓展土壤水分产品在高原水分循环和能量平衡模拟、植被生长与干旱监测的应用研究。     

结合参量统计与时空融合的土壤水分降尺度方法

土壤水分的降尺度研究为解决被动微波产品的粗分辨率问题,更好地服务于流域小尺度应用提供了技术手段。以美国俄克拉荷马州为研究区域,基于SMAP土壤水分产品和MODIS产品等多种辅助数据,在地表分类数据的支持下,结合参量统计降尺度和时空融合降尺度发展了一种土壤水分混合降尺度方法,并利用SMAP 9 km产品和站点实测数据对降尺度效果进行了评估。结果表明：混合降尺度方法可以得到细节丰富、空间覆盖完整的降尺度结果。相较于参量统计或时空融合两种单一降尺度而言,混合降尺度结果的空间分布与SMAP 9 km真实产品最为相似,并且混合降尺度结果与站点的整体时序精度最高,在不同地表分类下的时序精度也优于单一方法的降尺度结果。由此证明结合参量统计与时空融合的降尺度方法是可行的。     

SMAP土壤水分产品在淮河流域的适用性评估

选取淮河流域为研究区域,利用2016年6月至2019年5月流域内的313个土壤水分观测站0~10 cm土壤体积含水量数据,使用多种指标分析SMAP卫星（Soil Moisture Active Passive）9 km分辨率土壤水分产品（L2_SM_P_E）精度的空间和时间（年、月、日尺度）特征,并讨论植被、土壤、地形等对精度影响。结果表明：①整体来看,L2_SM_P_E在淮河流域达不到0.04 m³/m³的预期精度,存在湿区高估、干区低估的现象,但可以较好地反映流域土壤水分的空间分布特征,也能较为准确地指示高湿区和低湿区。②L2_SM_P_E的精度存在明显的区域差异和季节差异。冬季精度明显优于其他季节,流域大部分地区的无偏均方根误差（ubRMSE）均接近预期精度,且在流域北部的部分地区、伏牛山区和大别山区达到了预期精度。在春秋季,流域北部和大别山区的精度较高。夏季L2_SM_P_E的可用性较差。③L2_SM_P_E和降水有较好的一致性,对降水的响应比土壤水分观测值敏感。在降水过程中和降水结束后,L2_SM_P_E的误差以随机误差为主;当土壤相对干燥,则以系统性负偏差为主。④L2_SM_P_E的精度与采样点的土壤类型关系并不密切,山地地区的精度要优于其他地区。     

Evaluation of remotely sensed and modelled soil moisture products using global ground-based in situ observations

In situ soil moisture data from more than 200 stations located in Africa, Australia, Europe and the United States are used to determine the reliability of three soil moisture products, one analysis from the ECMWF (European Centre for Medium-Range Weather Forecasts) numerical weather prediction system (SM-DAS-2) and two remotely sensed soil moisture products, namely ASCAT (Advanced scatterometer) and SMOS (Soil Moisture Ocean Salinity). SM-DAS-2 is produced offline at ECMWF and relies on an advanced surface data assimilation system (Extended Kalman Filter) used to optimally combine conventional observations with satellite measurements. ASCAT remotely sensed surface soil moisture is provided in near real time by EUMETSAT. At ECMWF, ASCAT is used for soil moisture analyses in SM-DAS-2, also. Finally the SMOS remotely sensed soil moisture data level two product developed at CESBIO is used. Evaluation of the times series as well as of the anomaly values, shows good performances of the three products to capture surface soil moisture annual cycle and short term variability. Correlations with in situ data are very satisfactory over most of the investigated sites located in contrasted biomes and climate conditions with averaged values of 0.70 for SM-DAS-2, 0.53 for ASCAT and 0.54 for SMOS. Although radio frequency interference disturbs the natural microwave emission of the Earth observed by SMOS in several parts of the world, hence the soil moisture retrieval, performances of SMOS over Australia are very encouraging.     

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.     

基于微波遥感和陆面模型的流域土壤水分研究

土壤水分是陆地水文的重要因子。微波遥感是测量土壤水分的一种重要方法。本文总结了基于微波遥感和陆面模型的土壤水分监测方法,包括被动微波法、主动微波法、主被动微波结合法、陆面模型模拟法和数据同化法五种。被动微波对表面土壤水分敏感,但其空间分辨率低;主动微波具有较好的分辨率但运作费用也较高;主被动微波结合则能够充分利用各自的优势。陆面模型在研究中也有重要作用,通过模型模拟能够得到根区土壤水分。而将观测值同化到模型的数据同化法,则能极大的提高土壤水分估计的能力。通过比较,指出数据同化是最有前景的研究领域。     

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.     

亚洲区域AMSR2与SMOS土壤水分产品对比研究

遥感反演土壤水分(SM)产品越来越多地应用于农业、气象、水文等研究,而微波土壤水分数据产品的区域适用性分析是其合理使用的必要前提。使用MERRA-2(Modern Era Retrospective-analysis for Research and Applications,Version 2)模拟土壤水分为参考数据,运用传统统计方法(原始数据相关性、距平相关性、偏差以及无偏均方根差)和TC(Triple-Collocation)不确定性误差模型分析的方法,对亚洲区域2012年7月～2016年7月两种被动微波土壤水分SMOS-L3-SM(Soil Moisture and Ocean Salinity,L3)和AMSR2-LPRM-SM(The Advanced Microwave Scanning Radiometer 2,Land Parameter Retrieval Model Product)进行对比评估。结果表明:①空间上SMOS-L3较AMSR2-LPRM数据与参考数据MERRA-2土壤水分的相关性较好,表现为SMOS-L3-SM具有较好的空间连续性,且在亚洲大多数地区有较小的无偏均方根差;②湿季条件下遥感土壤水分与参考值的相关性比干季条件下的相关性更好,且干季出现高纬地区(约55°)缺失值较多的情况;③两遥感土壤水分的TC误差呈现相似的分布,区域TC平均误差两者均为0.076 m~3/m~3。总之,SMOS-L3-SM和AMSR2-LPRM-SM在空间相关性及TC误差评价方面都具有合理性,为遥感土壤水分在农业、气象、水文等方面的应用提供参考。     

被动微波遥感反演土壤水分进展研究

在地球系统中, 地表土壤水分是陆地和大气能量交换过程中的重要因子, 并对陆地表面蒸散、水的运移、碳循环有很强的控制作用, 大面积监测土壤水分在水文、气象和农业科学领域具有较大的应用潜力。被动微波遥感是监测土壤含水量最有效的手段之一, 相比红外与可见光, 它具有波长长, 穿透能力强的优势, 相比主动微波雷达, 被动微波辐射计具有监测面积大、周期短, 受粗糙度影响小, 对土壤水分更为敏感, 算法更为成熟的优势。然而微波辐射计观测到的亮温除了受土壤水分影响外, 还要考虑如植被覆盖、土壤温度、雪覆盖以及地形、地表粗糙度、土壤纹理和大气效应以及地表的异质性等其它因子的影响。目前, 已研究出许多使用被动微波辐射计反演土壤水分的方法,这些方法大部分是围绕着土壤湿度与亮温温度之间的关系进行, 同时也考虑其它各种不同因子对 地表微波辐射的影响。从介绍被动微波反演地表参数的原理入手, 重点介绍被动遥感反演土壤水分当前的算法进展、研究趋势等。     

An appraisal of the accuracy of operational soil moisture estimates from SMOS MIRAS using validated in situ observations acquired in a Mediterranean environment

Acquiring information on the spatio-temporal variability of soil moisture is of key importance in extending our capability to understand the Earth system’s physical processes, and is also required in many practical applications. Earth observation (EO) provides a promising avenue to observe the distribution of soil moisture at different observational scales, with a number of products distributed at present operationally. Validation of such products at a range of climate and environmental conditions across continents is a fundamental step related to their practical use. Various in situ soil moisture ground observational networks have been established globally providing suitable data for evaluating the accuracy of EO-based soil moisture products. This study aimed at evaluating the accuracy of soil moisture estimates provided from the Soil Moisture and Ocean Salinity Mission (SMOS) global operational product at test sites from the REMEDHUS International Soil Moisture Network (ISMN) in Spain. For this purpose, validated observations from in situ ground observations acquired nearly concurrent to SMOS overpass were utilized. Overall, results showed a generally reasonable agreement between the SMOS product and the in situ soil moisture measurements in the 0–5 cm soil moisture layer (root mean square error (RMSE) = 0.116 m³ m^?3). An improvement in product accuracy for the overall comparison was shown when days of high radio frequency interference were filtered out (RMSE = 0.110 m³ m^?3). Seasonal analysis showed highest agreement during autumn, followed by summer, winter, and spring seasons. A systematic soil moisture underestimation was also found for the overall comparison and during the four seasons. Overall, the result provides supportive evidence of the potential value of this operational product for meso-scale studies and practical applications.     

Analysis of in-situ soil moisture data and validation of SMOS soil moisture products at selected agricultural sites over a tropical region

Calibration and validation activities on Soil Moisture and Ocean Salinity (SMOS)-derived soil moisture products have been conducted worldwide since the data became available, but this has not been the case over tropical regions. This study focuses on the setting up of a soil moisture data collection network over an agricultural site in a tropical region in Peninsular Malaysia and on the validation of SMOS soil moisture products. The in-situ data over a one-and-a-half-year period was analysed and the validation of the SMOS soil moisture products with this in-situ data was conducted. Bias and root mean square error (RMSE) were computed between the SMOS soil moisture products and the in-situ surface soil moisture collected at the satellite passing times (6 am and 6 pm local time). Due to the known limitations of SMOS soil moisture retrieval over vegetated areas with a vegetation water content higher than 5 kg m^?2, an overestimation of SMOS soil moisture products to in-situ data was noticed in this study. The bias ranged from 0.064 to 0.119 m³ m^?3 and the RMSE was from 0.090 to 0.158 m³ m^?3, when both ascending and descending mode data were measured. This RMSE was found to be similar to those of a number of studies conducted previously at different regions. However, a wet bias was found during the validation, while previous validation activities at other locations showed dry biases. The result of this study is useful to support the continuous development and improvement of the SMOS soil moisture retrieval model, aiming to produce soil moisture products with higher accuracy, especially in tropical regions.     

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。     

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。     

RFI localization in synthetic aperture interferometric radiometers based on sparse Bayesian inference

The presence of radio frequency interference (RFI) sources emitting in the L-band, which is reserved for passive measurements by International Telecommunications Union (ITU) regulations, has seriously deteriorated the data quality of many brightness temperature (BT) snapshots in the Soil Moisture and Ocean Salinity (SMOS) project. In order to obviate the Gibbs-like contamination on the BT maps, one effective way is to locate the positions of RFI sources and switch them off. This article discusses a new method for RFI localization that is tailored to the scenario of synthetic aperture interferometric radiometry. The novel aspect lies in addressing the problem of RFI localization from a probabilistic viewpoint. By introducing the sparsity of RFI distribution in the spatial domain as a priori knowledge, we have employed the sparse Bayesian inference (SBI) strategy to estimate the locations of RFI sources. In addition, we have also tested the proposed method using numerical simulations and actual SMOS data. The results indicate that the proposed method has advantages in both accuracy and resolution of RFI source localization over the conventional direction-of-arrival (DOA) methods used in the beamforming technique.