Estimates of surface soil moisture under grass covers using L-band radiometry

The scope of this study is to establish the parameters of the L-band (1.4 GHz) Microwave Emission of the Biosphere model (L-MEB) for grass covers, and to assess surface soil moisture retrievals in areas covered by grass. L-MEB parameters are key ancillary information for the Soil Moisture and Ocean Salinity mission (SMOS) retrieval algorithm that produces estimates of the surface soil moisture from measurements of the surface brightness temperature at L-band.L-band data sets from three ground-based experiments over grass are analysed in this paper: BARC (orchard grass and alfalfa), ELBARA-ETH (clover grass), and SMOSREX (grass and litter from a field left fallow). Modelling of the brightness temperature using the zero-th order radiative transfer model in L-MEB indicates that the vegetation appears isotropic to microwaves propagating with horizontal polarisation, and that the single scattering albedo can be neglected. At vertical polarisation, non-zero scattering is observed for all the grass data sets. Surface soil moisture is retrieved with enough accuracy for all data sets as long as the soil and litter emission are calibrated beforehand. Then surface soil moisture and vegetation optical depth can be left as free parameters in the retrieval process. Finally, the study highlights the importance of detecting strong emission and attenuation by wet vegetation and litter due to rainfall interception in order to obtain accurate estimates of the surface soil moisture. The study illustrates how strong rainfall interception can be flagged straightforwardly using a microwave polarisation index.     

一种寒冷环境下分析幼林对地表微波辐射信号影响的方法

通过拓展冻土介电常数模型、寒冷环境植被介电常数模型,将Matrix-Doubling算法发展为适用于寒冷环境的幼林微波辐射模型。然后利用该模型模拟寒冷环境下幼林在Ku(18.7 GHz)、Ka(36.5 GHz)波段上的亮度温度并与车载微波辐射计获得的实测亮度温度对比,进行模型验证。最后,用发展后的Matrix\|Doubling模型模拟的结果和相同环境下ω-τ模型的结果进行匹配,反演出幼林在Ku、Ka波段上的等效单散射反照率和光学厚度,为用微波遥感监测地表冻融过程中去除植被对地表微波辐射的影响提供了一种有效方法。
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Effect of vegetation on soil moisture sensing observed from orbiting microwave radiometers

The microwave radiometric measurements made by the Skylab 1.4 GHz radiometer and by the 6.6 GHz and 10.7 GHz channels of the Nimbus-7 Scanning Multichannel Microwave Radiometer were analyzed to study the large-area soil moisture variations of land surfaces. Two regions in Texas, one with sparse and the other with dense vegetation covers, were selected for the study. The results gave a confirmation of the vegetation effect observed by ground-level microwave radiometers. Based on the statistics of the satellite data, it was possible to estimate surface soil moisture in about five different levels from dry to wet conditions with a 1.4 GHz radiometer, provided that the biomass of the vegetation cover could be independently measured. At frequencies greater than about 6.6 GHz, the radiometric measurements showed little sensitivity to moisture variation for vegetation-covered soils. The effects of polarization in microwave emission were studied also.     

Calculations of radar backscattering coefficient of vegetation-covered soils

A model for simulating the measured radar backscattering coefficient of vegetation-covered soil surfaces is presented in this study. The model consists of two parts: the first is a soil surface model to describe the backscattered radar pulses from a rough soil surface, and the second part takes into account the effect of vegetation cover. The soil surface is characterized by two parameters, the surface height standard deviation σ and the horizontal correlation length l. The effect of vegetation canopy scattering is incorporated into the model by making the radar pulse subject to two-way attenuation and volume scattering when it passes through the vegetation layer. These processes are characterized by the two parameters, the canopy optical thickness τ and the volume scattering factor η. The model results agree well with the measured angular distributions of the radar backscattering coefficient for HH polarization at the 1.6 GHz and 4.75 GHz frequencies over grass-covered fields. These observations were made from an aircraft platform during six flights over a grass watershed in Oklahoma. It was found that the coherent scattering from soil surfaces is very important at angles near nadir, while the vegetation volume scattering is dominant at larger incident angles (> 30°). The results show that least-squares fits to scatterometer data can provide reliable estimates of the surface roughness parameters, particularly the surface height standard deviation σ. The range of values for σ for the six flights is consistent with a 2 or 3 dB uncertainty in the magnitude of the radar response.     

A combined passive/active microwave remote sensing approach for surface variable retrieval using Tropical Rainfall Measuring Mission observations

The backscattering and emission measured simultaneously by radar and radiometer show promise for the estimation of surface variables such as near-surface soil moisture and vegetation characteristics. In this paper, the 10.7 GHz Tropical Rainfall Measuring Mission (TRMM) microwave imager (TMI) channel and 13.8 GHz precipitation radar (PR) observations are simultaneously used for the estimation of the near-surface soil moisture and vegetation properties. The Fresnel model for soil and a simple model for vegetation are used to simulate the passive microwave emission at 10.7 GHz. To determine the PR backscatter signal from a land surface, a theoretical approach is used based on the Geometric Optics Model for simulating bare soil and a semi-empirical water-cloud model for vegetation. The model parameters required in specifying the nature of the soil and vegetation are calibrated on the basis of in situ soil moisture data combined with remotely sensed observations. The calibrated model is subsequently used to retrieve near-surface soil moisture and leaf area index for assumed values of surface roughness and temperature. Algorithm assessment using synthetic passive and active microwave data shows a nonlinearity effect in the system inversion, which results in a varying degree of error statistics in soil wetness and vegetation characteristics retrieval. The technique was applied on TRMM radar/radiometer observations from three consecutive years and evaluated against in situ near-surface (5 cm) soil moisture measurements from the Oklahoma Mesonet showing a consistent performance.     

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.     

Effects of corn stalk orientation and water content on passive microwave sensing of soil moisture

A field experiment was conducted utilizing artificial arrangements of plant components during the summer of 1982 to examine the effects of corn canopy structure and plant water content on microwave emission. Truck-mounted microwave radiometers at C (5 GHz) and L (1.4 GHz) band sensed vertically and horizontally polarized radiation concurrent with ground observations of soil moisture and vegetation parameters. Results indicate that the orientation of cut stalks and the distribution of their dielectric properties through the canopy layer can influence the microwave emission measured from a vegetation/soil scene. The magnitude of this effect varies with polarization and frequency and with the amount of water in the plant, disappearing at low levels of vegetation water content. Although many of the canopy structures and orientations studied in this experiment are somewhat artificial, they serve to improve our understanding of microwave energy interactions within a vegetation canopy and to aid in the development of appropriate physically based vegetation models.     

Passive microwave sensing of soil moisture content: The effects of soil bulk density and surface roughness

Microwave radiometric measurements over bare fields of different surface roughness were made at frequencies of 1.4 GHz, 5 GHz, and 10.7 GHz to study the frequency dependence, as well as the possible time variation, of surface roughness. An increase in surface roughness was found to increase the brightness temperature af soils and reduce the slope of regression between brightness temperature and soil moisture content. The frequency dependence of the surface roughness effect was relatively weak when compared with that of the vegetation effect. Radiometric time-series observations over a given field indicate that field surface roughness might gradually diminish with time, especially after a rainfall or irrigation. The variation of surface roughness increases the uncertainty of remote soil moisture estimates by microwave radiometry. Three years of radiometric measurements over a test site revealed a possible inconsistency in the soil bulk density determination, which is an important factor in the interpretation of radiometric data.     

Comparing SMMR and AVHRR data for drought monitoring

Abstract

Coincident Scanning Microwave Multi-channel Radiometer 37 GHz and Advanced Very High Resolution Radiometer normalized difference vegetation index satellite data have been compared from drought-affected regions of sub-Saharan Africa and northeastern Brazil for the time period of 1980–1985. Although the two satellite data types can be highly correlated, differences between them were found for the Sahel zone in 1985 and for northeastern Brazil from 1984–1985. These findings suggest that scattering or surface roughness contributions may be greater than previously assumed for the 37 GHz microwave data. A programme of field measurements should be undertaken to increase our understanding of natural vegetation at 37 GHz and higher microwave frequencies.     

Microwave vegetation indices for short vegetation covers from satellite passive microwave sensor AMSR-E

Vegetation indices are valuable in many fields of geosciences. Conventional, visible-near infrared, indices are often limited by the effects of atmosphere, background soil conditions, and saturation at high levels of vegetation. In this study, we will establish the theoretical basis for our new passive microwave vegetation indices (MVIs) based on data from the Advanced Microwave Scanning Radiometer (AMSR-E) on the Aqua satellite. Through the analysis of numerical simulations by surface emission model, the Advanced Integral Equation Model (AIEM), we found that bare soil surface emissivities at different frequencies can be characterized by a linear function with parameters that are dependent on the pair of frequencies used. This makes it possible to minimize the surface emission signal and maximize the vegetation signal when using multi-frequency radiometer measurements. Using a radiative transfer model (ω–τ model), a linear relationship between the brightness temperatures observed at two adjacent radiometer frequencies can be derived. The intercept and slope of this linear function depend only on vegetation properties and can be used as vegetation indices. These can be derived from the dual-frequency and dual-polarization satellite measurements under assumption that there is no significant impact of the polarization dependence on the vegetation signals. To demonstrate the potential of the new microwave vegetation indices, we compared them with the Normalized Difference of Vegetation Index (NDVI) derived using MODIS the optical sensor at continental and global scales. The major purpose of this paper is to describe the concept and techniques involved in these newly developed MVIs and explore the general relationships between these MVIs and NDVI. In this first investigation, the information content of NDVI and MVIs, both are qualitative indices, was compared by examining its response in global pattern and to seasonal vegetation phenology. The results indicate that the MVIs can provide significant new information since the microwave measurements are sensitive not only to the leafy part of vegetation properties but also to the properties of the overall vegetation canopy when the microwave sensor can “see” through it. In combination with conventional optical sensor derived vegetation indices, they provide a possible complementary dataset for monitoring global short vegetation and seasonal phenology from space.     

Evaluation of emission from snow-covered ground for passive microwave remote sensing

This article investigates emission behaviour at frequencies of 18.7, 36.5 and 89 GHz and an incidence angle of 55° over a snow-covered surface at the local scale observation site in Fraser, CO, USA, using both one-layer and two-layer emission models. The models employ the matrix doubling approach to implement the radiative-transfer equation based on dense media theory and the advanced integral equation model. When compared to Ground-Based Passive Microwave Radiometer (GBMR-7) observation on 21 February 2003, both the models could simulate the observed brightness temperature well, but the polarization difference between the observation and the models was smaller for the two-layer emission model than the one-layer model. In addition, we successfully interpreted the emission magnitude and polarization separation of a snow-removed surface by incorporating a Mie scattering transition layer above the soil medium. In this work, we also demonstrated the effect of snow fraction on the brightness temperature difference at 18.7 and 36.5 GHz over a snow-covered surface with the field observation. In conclusion, we demonstrate the snow impact on soil surface with snow depth (SD) and snow fraction variation through modelling and in situ data.     

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.     

Spring thaw classification based on AMSR-E brightness temperature in the central Tibetan Plateau

Changes in soil freeze/thaw dynamics in the Tibetan Plateau (TP) had significant influences on regional hydrology and ecosystem processes. We developed an algorithm to detect spring thaw onset in the central TP using microwave brightness temperature (T_b) data. We assumed that the T_b at lower frequencies is more sensitive to changes in soil freeze/thaw status, while the T_b at higher frequencies is subjected more to scattering effects of snow cover and vegetation. Therefore, the standard deviation of brightness temperature at 6-GHz horizontal polarization was used to detect soil F/T status, and a scattering index based on higher frequencies was used to constrain the scattering effects of snow cover. The algorithm was calibrated and validated with surface ground temperature and daily minimum air temperature. Results showed that our algorithm had a mean bias of 3.7 ~ 17.6 days and a RMSE of 7.5 ~ 19.7 days, and this method behaved better than the previous one based on all-frequency standard deviation of microwave T_b, which had a mean bias of 8.3 ~ 41.2 days and a RMSE of 13.4 ~ 27.6 days. Further validation is needed over more extensive area with diverse surface conditions.     

Radiometric measurements over bare and vegetated fields at 1.4-GHz and 5-GHz frequencies

Results of radiometric measurements over bare and vegetated fields with dual-polarized microwave radiometers at 1.4-GHz and 5-GHz frequencies are presented. The measured brightness temperatures over bare fields are shown to compare favorably with those calculated from radiative transfer theory with two constant parameters characterizing surface roughness effect. The presence of vegetation cover is found to reduce the sensitivity to soil moisture variation. This sensitivity reduction is generally more pronounced the denser the vegetation cover and the higher the frequency of observation. The effect of vegetation cover is also examined with respect to the measured polarization factor at both frequencies. With the exception of dry corn fields, the measured polarization factor over vegetated fields is found appreciably reduced compared to that over bare fields. A much larger reduction in this factor is found at 5 GHz than at 1.4 GHz.     

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。     

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.     

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。     

Experimental data for separation of vegetation and soil and estimation of soil moisture using passive microwaves

Abstract

Microwave radiometer measurements of soil moisture content were made over bare and vegetated fields with dual polarized microwave radiometers at 1·55GHz (L-band) and 19·1 GHz (K.-band). Two typical Indian crops Bazra and Gawar have been studied. The bare field measurements were used to investigate the effect of soil texture on sensitivity of a radiometer to soil moisture and for soil moisture sampling depth. It is found that expression of soil moisture as available moisture content in the soil can minimize the texture effect. The estimated soil moisture sampling depth for L-band is 2-5 cm, while for K-band it is less than 2 cm. The vegetation cover affects the sensitivity of the radiometer to soil moisture. This effect is more pronounced the denser the vegetation and higher the frequency of observation. The measured polarization factor over a vegetated field at L-band was found to be appreciably reduced compared to that over a bare field. The difference between normalized brightness temperature from L-band and K-band is sensitive to vegetation type. The soil moisture under vegetation cover at L-band can be predicted well using Jackson's parametric model.     

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.     

Retrieving near-surface soil moisture from microwave radiometric observations: current status and future plans

Surface soil moisture is a key variable used to describe water and energy exchanges at the land surface/atmosphere interface. Passive microwave remotely sensed data have great potential for providing estimates of soil moisture with good temporal repetition on a daily basis and on a regional scale (∼10 km). However, the effects of vegetation cover, soil temperature, snow cover, topography, and soil surface roughness also play a significant role in the microwave emission from the surface. Different soil moisture retrieval approaches have been developed to account for the various parameters contributing to the surface microwave emission. Four main types of algorithms can be roughly distinguished depending on the way vegetation and temperature effects are accounted for. These algorithms are based on (i) land cover classification maps, (ii) ancillary remote sensing indexes, and (iii) two-parameter or (iv) three-parameter retrievals (in this case, soil moisture, vegetation optical depth, and effective surface temperature are retrieved simultaneously from the microwave observations). Methods (iii) and (iv) are based on multiconfiguration observations, in terms of frequency, polarization, or view angle. They appear to be very promising as very few ancillary information are required in the retrieval process. This paper reviews these various methods for retrieving surface soil moisture from microwave radiometric systems. The discussion highlights key issues that will have to be addressed in the near future to secure operational use of the proposed retrieval approaches.