Effects of sub-pixel heterogeneity on the retrieval of soil moisture from passive microwave radiometry

A simple formulation relating the L-band microwave brightness temperature detected by a passive microwave radiometer to the near surface soil moisture was developed using MICRO-SWEAT, a coupled microwave emission model and soil-vegetation-atmosphere-transfer (SVAT) scheme. This simple model provides an ideal tool with which to explore the impact of sub-pixel heterogeneity on the retrieval of soil moisture from microwave brightness temperatures. In the case of a bare soil pixel, the relationship between apparent emissivity and surface soil moisture is approximately linear, with the clay content of the soil influencing just the intercept of this relationship. It is shown that there are no errors in the retrieved soil moisture from a bare soil pixel that is heterogeneous in soil moisture and texture. However, in the case of a vegetated pixel, the slope of the relationship between apparent emissivity and surface soil moisture decreases with increasing vegetation. Therefore for a pixel that is heterogeneous in vegetation and soil moisture, errors can be introduced into the retrieved soil moisture. Generally, under moderate conditions, the retrieved soil moisture is within 3% of the actual soil moisture. Examples illustrating this discussion use data collected during the Southern Great Plains '97 Experiment (SGP97).     

Simulating the effect of spatial variability of soil hydraulic properties on microwave brightness temperature over a corn field

Spatial variability of L-band (21?cm wavelength) microwave brightness temperature over a corn field, caused by spatial heterogeneity of soil hydraulic properties, is simulated by combining physically based models for microwave emission and for dynamics of soil water. The scaling theory is used for the spatial variability of soil hydraulic parameters, the scaling parameter being represented by a histogram corresponding to a log-normal frequency distribution. The mean and the standard deviation of brightness temperatures over a corn field are calculated as a saturated soil dries progressively under clear-sky conditions. Results are presented for two values for the coefficient of variation (CV)of the scaling parameter, namely 0·45 and 0·65, which encompass the range of a few available field observations. For CV=0·45, the mean brightness temperatures are higher and the standard deviations are lower by about 2 deg K compared with those for CV = 0·65. Results of the present simulation suggest that spatial variability of hydraulic parameters might not be an important consideration for interpreting mean brightness temperatures over reasonably large (a few hectares or larger)vegetated fields, although some information about the frequency distribution of hydraulic parameters would be needed in interpreting the standard deviation of the brightness temperature.     

Investigation of effect of heterogeneities in vegetation and rainfall on simulated SSM/I brightness temperatures

Land surface characteristics: soil and vegetation and rainfall inputs are distributed in nature. Representation of land surface characteristics and inputs in models is lumped at spatial scales corresponding to the grid size or observation density. Complete distributed representation of these characteristics or inputs is infeasible due to excessive computational costs or costs associated with maintaining dense observational networks. The measurements of microwave brightness temperatures by the SSM/I (Special Sensor Microwave Imager) are at resolutions of the order of 56km 56km for 19 GHz and 33 km 33 km for 37 GHz. At these resolutions, soil moisture and vegetation are not homogeneous over the measurement area. The experiments carried out in this study determine the effect of heterogeneities in vegetation (leaf area index) and input rainfall on simulated soil moisture and brightness temperatures and the inversion of brightness temperatures to obtain soil moisture estimates. This study would help us to understand the implications of using the SSM/I microwave brightness temperatures for soil moisture estimation. The consequences of treating rainfall inputs and vegetation over large land surface areas in a lumped fashion is examined. Simpler methods based on dividing the leaf area index or input rainfall into classes rather than explicit representation for representing heterogeneities in leaf area index and spatial distribution of rainfall is tested. It is seen that soil moisture is affected by the representation (lumped vs distributed) of rainfall and not leaf area index. The effect of spatially distributed soil moisture on the inversion of observed SSM/I brightness temperatures to obtain soil moisture estimates is investigated. The inversion process does not exhibit biases in the retrieval of soil moisture. The methodology presented in this paper can be used for any satellite sensor for purposes of analysis and evaluation.     

Land cover-based optimal deconvolution of PALS L-band microwave brightness temperatures

An optimal deconvolution (ODC) technique has been developed to estimate microwave brightness temperatures of agricultural fields using microwave radiometer observations. The technique is applied to airborne measurements taken by the Passive and Active L and S band (PALS) sensor in Iowa during Soil Moisture Experiments in 2002 (SMEX02). Agricultural fields in the study area were predominantly soybeans and corn. The brightness temperatures of corn and soybeans were observed to be significantly different because of large differences in vegetation biomass. PALS observations have significant over-sampling; observations were made about 100 m apart and the sensor footprint extends to about 400 m. Conventionally, observations of this type are averaged to produce smooth spatial data fields of brightness temperatures. However, the conventional approach is in contrast to reality in which the brightness temperatures are in fact strongly dependent on land cover, which is characterized by sharp boundaries. In this study, we mathematically deconvolve the observations into brightness temperature at the field scale (500-800 m) using the sensor antenna response function. The result is more accurate spatial representation of field-scale brightness temperatures, which may in turn lead to more accurate soil moisture retrieval.     

Vegetation and surface roughness effects on AMSR-E land observations

Characteristics of the land surface including soil moisture, vegetation cover, and soil roughness among others influence the microwave emissivity and brightness temperature of the surface as observed from space. Knowledge of the variability of microwave signatures of vegetation and soil roughness is necessary to separate these influences from those of soil moisture for remote sensing applications to global hydrology and climate. We describe here a characterization of vegetation and soil roughness at the frequencies and spatial resolution of the EOS Aqua Advanced Microwave Scanning Radiometer (AMSR-E). A single parameter has been used to approximate the combined effects of vegetation and roughness. AMSR-E data have been analyzed to determine the frequency dependence of this parameter and to generate a global vegetation/roughness map and an estimate of seasonal variability. A physical model is used for the analysis with approximations appropriate to the AMSR-E footprint scale and coefficients calibrated empirically against the AMSR-E data. The spatial variabilities of roughness and vegetation cannot be estimated independently using this approach, but their temporal dynamics allow separation of predominantly static roughness effects from time-varying vegetation effects using multitemporal analysis. Global signals of time-varying vegetation water content derived from this analysis of AMSR-E data are consistent with time-varying biomass estimates obtained by optical/infrared remote sensing techniques.     

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.     

Improving Soil Moisture Estimation by Joint Assimilationof MODIS Land Surface Temperature and Airborne L band Microwave Brightness Temperature

In this work,a novel soil moisture data assimilation scheme was developed,which was based land surface model (CoLM,Common Land Model),microwave radioactive transfer model (L MEB,L band Microwave Emission of the Biosphere),and data assimilation algorithm (EnKS,Ensemble Kalman Smoother).This scheme is used to improve the estimation of soil moisture profile by jointly assimilatingMODIS land surface temperature and airborne L band passive microwave brightness temperature.The ground based data observed at DAMAN superstation,which is located at Yingke oasis desert area in the middle stream of the Heihe River Basin,are used to conduct this experiment and validate assimilation results.Three LAI products are used to analyze the influence of LAI on soil temperature.Three assimilation experiments are also designed in this work,including assimilation of MODIS LST,assimilation of microwave brightness temperature,and assimilation of MODIS LST and microwave brightness temperature.The results show that the uncertainties in LAI influence significantly soil temperature simulations in different soil layers.MODIS LAI product is seriously underestimated in this study area,which results soil temperature overestimation about 4~6 K.Three assimilation schemes can improve soil moisture estimations to different extend.Joint assimilation of MODIS LST and microwave brightness temperature achieved the best performance,which can reduce the RMSE of soil moisture to 31%~53%.     

Spatial and temporal variability of soil moisture over India using IRS P4 MSMR data

The brightness temperature data measured by the multi‐frequency scanning microwave radiometer (MSMR) data has been analysed over the Indian subcontinent to deduce the seasonal and monthly variations of soil moisture. The present results show the spatial variations of soil moisture over the Indian region which is affected by the monsoon and show strong variability over different geological terrains.     

一种一维综合孔径微波辐射计的定标方法

综合孔径微波辐射计是被动微波遥感发展的新方向。综合孔径技术利用了以小口径天线阵列合成大的观测口径的技术，解决了在较低频率时天线物理口径要足够大才能得到期望的空间分辨率的严重缺陷。土壤湿度和海水盐度是影响全球气候和水气循环的重要因素。这些参数一般是在L波段范围观测得到，综合孔径辐射计就是减少天线孔径和重量，并最终可以观测反演出这两个参数的一项新兴技术。综合孔径辐射计不同于全功率辐射计，它测量的是视场亮温分布对于天线阵中不同基线长度的可视度函数分量。它的系统主体是稀疏天线阵和多通道相关接收机。在实际应用中，要得到土壤湿度等参数的反演，较高的系统亮温分辨率以及亮温与测量量之间的准确对应是至关重要的，这即是定标工作要完成的任务。定标直接影响微波辐射图解译和判读的准确度，是实现定量化微波遥感的前提。针对一雏综合孔径辐射计系统，给出了一种定标方案。其中分析了天线阵以及多通道相关接收机部分的定标，由得到的矩阵形式的空间频率响应信息推出了亮温图像的反演公式。     

Identification of dry and wet soil conditions using TRMM/TMI brightness temperatures and potential for drought monitoring

In this study we present a methodology for monitoring drought conditions directly from microwave brightness temperature observations. Tropical Rainfall Measurement Mission (TRMM)/TRMM Microwave Imager (TMI) 10.7 GHz brightness temperatures were analysed along with TRMM merged rainfall products during June–August for 4 years to depict the spatial and temporal extent of dry and wet soil conditions. Comparison of brightness temperature anomalies with rainfall anomalies clearly shows the contrasting features of drought year 2002 and normal monsoon year 2001.     

微波月亮-人类对月球的全新视角——中国“嫦娥一号”卫星微波探测仪若干探测结果

中国“嫦娥一号”探月卫星自2007年10月24日成功发射并于同年11月7日进入其工作轨道。在轨工作一年多,完成了全部使命,期间获取了大量的科学数据。其中“嫦娥一号”月球微波探测仪(Chang’e-1 Lunar Microwave Sounder-CELMS)已多次覆盖全月表面,首次获取了全月微波亮温分布数据,创建了“微波月亮”(Microwave Moon-MicM)。“微波月亮”的建立为月球科学研究、宇宙科学研究、月球资源研究及应用、未来月球基地的建立等带来了全新的信息,与“可见月亮”、“红外月亮”及其它相关探测结果(如X、γ谱仪,中子谱仪)、地基探测及未来月球轨道上观测和就位探测等多方信息的融合、分析,将大大提升人类对太空、月球及宇宙起源、生命起源等问题的认识和研究水平,在人类探月活动中具有里程碑意义。
在“嫦娥一号”卫星微波探测仪绕月探测之前,从来没有从月球轨道对全月球进行微波探测的活动。很多涉及月球微波特征研究,如月表微波亮温分布、月壤厚度及氦-3资源量分布信息、涉及月球历史等的研究多数是靠Apollo、Luna的落月点实测数据为依据,加上其它探测(如光学等)结果融合分析并逻辑延伸而得来的,因此其结果存在相当的多解或不确定性,这就使我们对月球微波辐射特性的真实情况了解很少,甚至可能有偏差。着重讨论“微波月亮”的含义,其相关信息内涵,几种特征区、点的分析等。根据微波探测仪的数据,获得了全月月壤厚度分布、氦-3资源量评估、全月亮温及其变化规律等研究结果,得出了一些与现今其它研究结果不同的结论。     

A soil moisture assimilation scheme based on the microwave Land Emissivity Model and the Community Land Model

Applications of microwave remote-sensing data in land data assimilation are a topic of current interest and importance due to their high temporal and spatial resolution and availability. However, there have been few studies on land surface sub-grid scale heterogeneity and calculating microwave wetland surface emissivity when directly assimilating gridded Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) satellite brightness temperature (BT) data to estimate soil moisture. How to assimilate gridded AMSR-E BT data for land surface model (LSM) grid cells including various land cover types, especially wetland, is worthy of careful study. The ensemble Kalman filter (EnKF) method is able to resolve the non-linearity and discontinuity in forecast and observation operators, and is widely used in land data assimilation. In this study, considering the influences of land surface sub-grid scale heterogeneity, a satellite data simulation scheme based on the National Center for Atmosphere Research (NCAR) Community Land Model version 2.0 (CLM2.0), microwave Land Emissivity Model (LandEM), Shuffled Complex Evolution (SCE-UA) algorithm and AMSR-E BT observation data is presented to simulate AMSR-E BT data and calibrate microwave wetland surface emissivity; then, a soil moisture data assimilation scheme is developed to directly assimilate the gridded AMSR-E BT data, which consists of the CLM2.0, LandEM and EnKF. The experimental results indicate that the calibrated microwave wetland surface emissivities possess excellent transportability, and that the assimilation scheme is practical and can significantly improve soil moisture estimation accuracy. This study provides a promising solution to improve soil moisture estimation accuracy through directly assimilating gridded AMSR-E BT data for various land cover types such as bare soil, vegetation, snow, lake and wetland.     

Effects of noise on optimal deconvolution accuracy in microwave observations

Due to large footprints of remotely sensed microwave brightness temperatures, accuracy of microwave observations in areas of large surface heterogeneity has always been a technological challenge. Microwave observations in areas dominated by waterbodies typically exhibit observed brightness temperature several tens of kelvins lower than areas having no surface water. The non-linearity between brightness temperature and other geophysical quantities such as soil moisture makes the accuracy of microwave observations a critical element for accurate estimation of these quantities. In retrieving soil moisture estimates, an error of 1 K in remotely sensed microwave brightness temperatures results in about 0.5–1% error in volumetric soil moisture. Large uncertainties in the observed brightness temperatures make such observations unusable in areas of large brightness temperature contrast. In this article, we discuss a deconvolution method to improve accuracy using the overlap in the adjacent microwave observations. We have shown that the method results in improved accuracy of 40% in brightness temperature estimation in regions of high brightness temperature contrast.     

Further validation of a new methodology for surface moisture and vegetation optical depth retrieval

A series of validation studies for a recently developed soil moisture and optical depth retrieval algorithm is presented. The approach is largely theoretical, and uses a non-linear iterative optimization procedure to solve a simple radiative transfer equation for the two parameters from dual polarization satellite microwave brightness temperatures. The satellite retrievals were derived from night-time 6.6?GHz Nimbus Scanning Multichannel Microwave Radiometer (SMMR) observations, and were compared to soil moisture data sets from the USA, Mongolia, Turkmenistan and Russia. The surface temperature, which is also an unknown parameter in the model, is derived off-line from 37?GHz vertical polarized brightness temperatures. The new theoretical approach is independent of field observations of soil moisture or canopy biophysical measurements and can be used at any wavelength in the microwave region. The soil moisture retrievals compared well with the surface moisture observations from the various locations. The vegetation optical depth also compared well to time series of Normalized Difference Vegetation Index (NDVI) and showed similar seasonal patterns. From a global perspective, the satellite-derived surface soil moisture was consistent with expected spatial patterns, identifying both known dry areas such as deserts and semi-arid areas and moist agricultural areas very well. Spatial patterns of vegetation optical depth were found to be in agreement with NDVI. The methodology described in this study should be directly transferable to the Advanced Microwave Scanning Radiometer (AMSR) on the recently launched AQUA satellite.     

A novel ground-based experimental observation method for determining multiband microwave radiative properties of forest litter

The layer of litter covering the forest floor attenuates microwave radiation coming from soil. In satellite remote-sensing data, this reduces the sensitivity of brightness temperature to land surface parameters (e.g. soil moisture, snow depth, and snow water equivalent), resulting in poorer inversion accuracy. To quantify the effects of microwave radiative properties of litter at different frequencies, and especially the impact on transmissivity, a novel approach was developed for modelling radiative transfer (RT) through litter. This approach is based on a zero-order RT model that accounts for scattering effects (the τ–ω model, τ is the optical thickness; ω is the single scattering albedo). Controlled ground-based experiments were conducted to obtain brightness temperatures at several frequencies (1.4, 18.7, and 36.5 GHz) as affected by the thickness and weight moisture content of the litter. The effects of measurement errors on transmissivity were then evaluated. This novel method, which is not only based on sound theory but also prevents calibration errors, can be used to obtain parameters such as the extinction coefficient and transmissivity. The results of this study provide new insights into the microwave RT theory of forest systems, allowing for more appropriate brightness temperatures corrections for satellites data, and providing a guide for controlled experiments.     

The relationship between brightness temperature and soil moisture Selection of frequency range for microwave remote sensing

Abstract

The analysis of brightness temperature data acquired from field and aircraft experiments demonstrates a linear relationship between soil moisture and brightness temperature. However, the analysis of brightness temperature data acquired by the Skylab radiometer demonstrates a non-linear relationship between soil moisture and brightness temperature. In view of the above and also because of recent theoretical developments for the calculation of the dielectric constant and brightness temperature under varying soil moisture profile conditions, an attempt is made to study the theoretical relationship between brightness temperature and soil moisture as a function of frequency. Through the above analysis, the appropriate microwave frequency range for soil moisture studies is recommended.     

Emissivity peculiarities of the inland salt marshes in the south of Western Siberia

The presence of water-dissoluble salts in soils leads to a wide variation in soil microwave radiation that makes precise determination of water-physical characteristics of soil using satellite data hardly possible. To assess the effect of soil salinization, the emissivity of salt-affected soils in the Kulunda plain located in the south of Western Siberia was studied. Steppe site and an inland salt marsh (ISM) formed at the bottom of depressions of dried-up salt lakes were the main objects of the research. Both sites are situated in the same natural-climatic conditions and have similar relief. The spatial distribution of the underlying surface brightness temperature was obtained from L1C product of Soil Moisture and Ocean Salinity (SMOS) mission. The diurnal temperature variations of ISM were measured in the course of a daily ground-based experiment to determine the temperature gradients. During the laboratory experiment, we defined the dielectric properties of soil and calculated its emissivity. The comprehensive studies are evidence of great diurnal variation in ISM microwave radiation. The joint use of satellite and laboratory data allows us to specify the contribution of salt-affected soils into total microwave radiation of the underlying surface. The brightness temperature obtained from SMOS and the one calculated from the laboratory dependencies of ISM emissivity on temperature and moisture are in fairly well correlation.     

Soil moisture mapping and AMSR-E validation using the PSR in SMEX02

Field experiments (SMEX02) were conducted to evaluate the effects of dense agricultural crop conditions on soil moisture retrieval using passive microwave remote sensing. Aircraft observations were collected using a new version of the Polarimetric Scanning Radiometer (PSR) that provided four C band and four X band frequencies. Observations were also available from the Aqua satellite Advanced Microwave Scanning Radiometer (AMSR-E) at these same frequencies. SMEX02 was conducted over a three-week period during the summer near Ames, Iowa. Corn and soybeans dominate the region. During the study period the corn was approaching its peak water content state and the soybeans were at the mid point of the growth cycle. Aircraft observations are compared to ground observations. Subsequently models are developed to describe the effects of corn and soybeans on soil moisture retrieval. Multiple altitude aircraft brightness temperatures were compared to AMSR-E observations to understand brightness temperature scaling and provide validation. The X-band observations from the two sensors were in reasonable agreement. The AMSR-E C-band observations were contaminated with anthropogenic RFI, which made comparison to the PSR invalid. Aircraft data along with ancillary data were used in a retrieval algorithm to map soil moisture. The PSR estimated soil moisture retrievals on a field-by-field comparison had a standard error of estimate (SEE) of 5.5%. The error reduced when high altitude soil moisture estimates were aggregated to 25 km resolution (same as AMSR-E EASE grid product resolution) (SEE ∼ 2.85%). These soil moisture products provide a validation of the AMSR retrievals. PSR/CX soil moisture images show spatial and temporal patterns consistent with meteorological and soil conditions. The dynamic range of the PSR/CX observations indicates that reasonable soil moisture estimates can be obtained from AMSR, even in areas of high vegetation biomass content (∼ 4-8 kg/m2).     

AMSR-E data inversion for soil temperature estimation under snow cover

Climate warming is the focus of several studies where the soil temperature plays an essential role as a state variable for the surface energy balance of the Earth. Many methods have been developed to determine summer surface temperature, but the determination in presence of snow is an ill-conditioned problem for microwave techniques because snow changes the emissivity of the surface. This project aims to improve the estimation of soil temperature, within the top 5 cm of the ground, under the snowpack using passive microwave remote sensing. Results show the potential of the passive microwave brightness temperature inversion at 10 GHz (derived from the Advanced Microwave Scanning Radiometer—Earth Observing System, AMSR-E) for the estimation of soil temperature using a physical multilayer snow-soil model (SNTHERM) coupled with a snow emission model (HUT). The snow model is driven with meteorological measurements from ground-based stations as well as data generated from reanalysis. The proposed iterative retrieval method minimizes the difference between the simulated and measured brightness temperature using the soil temperature as a free parameter given by SNTHERM. Results are validated against ground-based measurements at several sites across Canada through several winter seasons. The overall root mean square error and bias in the retrieved soil temperature is respectively 3.29 K and 0.56 K, lower than the error derived from the snow-soil model without the use of remote sensing. The accuracy in detection of frozen/unfrozen soil under the snowpack is 78%, which is improved up to 81% if the spring melting period is not considered. This original procedure constitutes a very promising tool to characterize the soil (frozen or not) under snow cover, as well as its evolution in northern remote locations where measurements are unavailable.     

Microwave emission of soil freezing and thawing observed by a truck-mounted microwave radiometer

This article presents a study of the interference effect of the microwave emission of soil during freezing and thawing processes. The microwave brightness temperature (T _B) was measured at the C (6.925 GHz), X (10.65 GHz), K (18.7 GHz) and K_a (36.5 GHz) bands using a truck-mounted dual-polarized microwave radiometer. Obvious T _B oscillation behaviour was shown in the results, which were compared with both coherent and non-coherent emission models. The characteristics of the measured and modelled results were similar, except for the oscillation frequency and amplitude. This was attributed to the error in estimation of the dielectric constant of frozen soil and some other factors. This effect was important in analysing the experimental data.