A fully software Global Navigation Satellite System reflectometry (GNSS-R) receiver for soil monitoring

Global Navigation Satellite System reflectometry (GNSS-R) is an emerging technique used for the remote sensing of soil surfaces. In this work, a bistatic radar equation method was implemented for soil moisture remote sensing using GNSS-R. Both direct and reflected GPS signals were collected by a fully software receiver mounted onboard an aircraft. The reflected signal was processed with an open loop approach, obtaining delay-Doppler maps (DDMs) and the corresponding delay waveforms. Signal to noise ratio (SNR) time series were estimated from non-coherently integrated delay waveforms. To take into account variations in system parameters from nominal values, a calibration was performed in regard to signals reflected from a water lake. Finally, a retrieval process to estimate the permittivity of soil surfaces from the evaluated SNR was applied and the results showed good correlation with the status of field flooding.     

基于Bi-Mimics模型的GNSS-R农作物生物量监测理论研究

GNSS-R(GNSS-Reflectometry)遥感从机理上讲属于双站雷达,在一阶辐射传输方程Mimics(Michigan Microwave Canopy Scattering)模型的基础上,将其修改为双站散射模型Bi-Mimics(Bistatic\|Mimics);将模型中的树干层去掉,保留树冠层和地表间的散射机制,发展了适用于农作物的Bi-Mimics模型。利用该模型,模拟分析在GNSS-R工作的L波段农作物的散射特性;根据GNSS-R设置模拟分析了镜像散射系数与农作物生物量之间的关系;并根据双站雷达理论公式,模拟了农作物生物量与接收机信号之间的关系。结果表明GNSS-R从理论上用来研究和监测农作物的生物量存在可行性,但研究工作有待进一步深入。     

Sensitivity of bistatic scattering to soil moisture and surface roughness of bare soils

The sensitivity of bistatic scattering coefficient σ° to soil moisture content (SMC) and surface roughness was investigated by means of model simulations of the incoherent scattered fields performed with the advanced integral equation model (AIEM) and the second order small perturbation model (SPM). The study was performed by simulating scattering on the whole upper half space, for different values of incident angles. The achieved results, represented as maps of σ° as a function of azimuth and zenith angles, were evaluated by means of a quality index which takes into consideration the effect of roughness on SMC measurement. The sensitivity analysis has pointed out that for measuring SMC a bistatic observation, by itself or combined with the monostatic one, can make appreciable improvements with respect to classical monostatic radar. Appendix A contains the AIEM formulas corrected for several typographical errors present in the specific literature.     

Utilizing space-based GPS technology to determine hydrological properties of soils

Competition for limited water resources is one of the most critical issues being faced by irrigated agriculture in the United States. Site-specific irrigation applies irrigation water to match the needs of individual management zones within a field, significantly reducing water consumption, runoff, and nutrient leaching in ground water. Remote sensing for real-time and continuous soil moisture measurements at specific depths is essential for success of site-specific irrigation system.The overall objective of this study was to investigate the feasibility of utilizing a GPS-based sensor technology to determine site-specific information such as the soil moisture condition by recording the GPS signal reflected from the earth's surface. A modified GPS Delay Mapping Receiver (DMR) tracks and measures the direct, line-of-sight, Right-Hand-Circularly Polarized signal of a GPS satellite. It also simultaneously measures the delayed, earth-reflected, near-specular, Left-Hand-Circularly Polarized GPS signal. These measurements can be used to estimate the surface scattering coefficient and path delays between the direct and reflected GPS signals. Over land, scattering coefficients can be used to estimate changes in soil moisture contents.Our results showed that the space-based technology has a great potential for determining soil volumetric moisture contents in the pursuit of site-specific irrigation management. There were strong correlations between the GPS reflectivity measurements and soil moisture contents. The GPS reflectivity increased as the soil moisture contents increased. Careful analysis of the test data showed very conclusively that the sensitivity of L-Band signal (1.575 GHz) to soil moisture contents changed with soil type and sampling depth. The sensitivity decreased with sampling depth in light soils and increased in heavy soils.     

Soil moisture mapping based on ASAR/ENVISAT radar data over a Sahelian region

The analysis of feedbacks between continental surfaces and the atmosphere is one of the key factors to understanding African Monsoon dynamics. For this reason, the monitoring of surface parameters, in particular soil moisture, is very important. Satellite remote sensing appears to be the most suitable means of obtaining data relevant to such parameters. The present paper presents a methodology applied to the mapping and monitoring of surface soil moisture over the Kori Dantiandou region in Niger, using data provided by the ASAR/ENVISAT radar instrument. The study is based on 15 sets of ASAR/ENVISAT C‐band radar data, acquired during the 2004 and 2005 rainy seasons. Simultaneously with radar acquisitions, ground soil moisture measurements were carried out in a large number of test fields. Soil moisture was estimated only for fields with bare soil or low‐density vegetation, using low‐incidence‐angle radar data (IS1 configuration). A mask was developed, using SPOT/HRV data and DTM, for use over areas characterized by high‐density vegetation cover, pools, and areas with high slopes. Soil moisture estimations are based on horizontal‐ and vertical‐polarization radar data. In order to double the temporal frequency of soil moisture estimations, IS2 data were used with IS1 data, with all data normalized to a single incidence angle. A high correlation is observed between in situ measurements and processed radar data. An empirical inversion technique is proposed, to estimate surface soil moisture from dual‐polarization data with a spatial resolution of approximately 1 km. Surface soil moisture maps are presented for all the studied sites, at various dates in 2004 and 2005. Of particular interest, these maps reveal convective precipitation scales associated with strong spatial variations in surface soil moisture.     

Cover photograph Seasat radar image of the Phoenix,Arizona, Region

Abstract

A high spatial resolution HF radar operating in the skywave mode has been used to map a warm water eddy and the Gulf Stream in the North Atlantic ocean by means of remotely sensing the current structure of the eddy. A bistatic system was used, transmission being provided by the NRL MADRE radar, in Chesapeake Beach, Maryland, while reception of the backscattered signal was carried out with the ONR 1100m array at Whitehouse, Virginia, some 80 miles south of the transmission site. Currents were measured by determining the shifts of the Bragg lines of the scattered signal due to the translation of the water mass in a given radar scattering cell, which measured typically 6 km in range and 12 km in azimuth. The eddy was located approximately 1200 km from the east coast; two-way E-layer propagation was used for radar coverage to this range. Path quality was sufficiently stable to allow radial velocity resolution of the order of 12cm/s, using coherent integration times of 100 s. This technique offers a capability not available with infrared setallite sensors, which measure thermal features that can be masked by several causes.     

Effective soil moisture sampling depth of L-band radiometry: A case study

The aim of this study is to analyze the influence of the soil moisture sampling depth in the parameterization of soil emission in microwave radiometry at L-band. The analysis is based on brightness temperature, soil moisture and temperature measurements acquired over a bare soil during the SMOSREX experiment. A more detailed profile of surface soil moisture was obtained with a soil heat and water flows mechanistic model. It was found that (1) the soil moisture sampling depth depends on soil moisture conditions, (2) the effective soil moisture sampling depth is shallower than provided by widely used field moisture sensors, and (3) the soil moisture sampling depth has an impact on the calibration of soil roughness model parameters. These conclusions are crucial for the calibration and validation of remote sensing data at L-band.     

Active microwave measurement of soil water content

Measurements of radar backscatter from bare soil at 4.7, 5.9, and 7.1 GHz for incident angles of 0–70° have been analyzed to determine sensitivity to soil moisture. Because the effective depth of penetration of the radar signal is only about one skin depth, the observed signals were correlated with the moisture in a skin depth as characterized by the attenuation coefficient (reciprocal of skin depth). Since the attenuation coefficient is a monotonically increasing function of moisture density, it may also be used as a measure of moisture content over the distance involved, which varies with frequency and moisture content. The measurements show an approximately linear increase in scattering with attenuation coefficient of the soil at angles within 10° of vertical and all frequencies. At 4.7 GHz this increase continues relatively large out to 70° incidence, but by 7.1 GHz the sensitivity is much less even at 20° and practically gone at 50°.An inversion technique to determine how well the moisture content can be estimated from the scattered signal indicates good success for near-vertical angles and middle ranges of moisture density, with poorer success at smaller moisture densities and an anomaly in the data at the highest moisture density that must be resolved by further experimentation.     

Utilizing calibrated GPS reflected signals to estimate soil reflectivity and dielectric constant: Results from SMEX02

Extensive reflected GPS data was collected using a GPS reflectometer installed on an HC130 aircraft during the Soil Moisture Experiment 2002 (SMEX02) near Ames, Iowa. At the same time, widespread surface truth data was acquired in the form of point soil moisture profiles, areal sampling of near-surface soil moisture, total green biomass and precipitation history, among others. Previously, there have been no reported efforts to calibrate reflected GPS data sets acquired over land. This paper reports the results of two approaches to calibration of the data that yield consistent results. It is shown that estimating the strength of the reflected signals by either (1) assuming an approximately specular surface reflection or (2) inferring the surface slope probability density and associated normalization constants give essentially the same results for the conditions encountered in SMEX02. The corrected data is converted to surface reflectivity and then to dielectric constant as a test of the calibration approaches. Utilizing the extensive in-situ soil moisture related data this paper also presents the results of comparing the GPS-inferred relative dielectric constant with the Wang-Schmugge model frequently used to relate volume moisture content to dielectric constant. It is shown that the calibrated GPS reflectivity estimates follow the expected dependence of permittivity with volume moisture, but with the following qualification: The soil moisture value governing the reflectivity appears to come from only the top 1-2 cm of soil, a result consistent with results found for other microwave techniques operating at L-band. Nevertheless, the experimentally derived dielectric constant is generally lower than predicted. Possible explanations are presented to explain this result.     

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.     

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...     

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.     

Temporal and spatial soil moisture change pattern detection in an agricultural area using multi‐temporal Radarsat ScanSAR data

Monitoring the characteristics of spatially and temporally distributed soil moisture is important to the study of hydrology and climatology for understanding and calculating the surface water balance. The major difficulties in retrieving soil moisture with Synthetic Aperture Radar (SAR) measurements are due to the effects of surface roughness and vegetation cover. In this study we demonstrate a technique to estimate the relative soil moisture change by using multi‐temporal C band HH polarized Radarsat ScanSAR data. This technique includes two components. The first is to minimize the effects of surface roughness by using two microwave radar measurements with different incidence angles for estimation of the relative soil moisture change defined as the ratio between two soil volumetric moistures. This was done by the development of a semi‐empirical backscattering model using a database that simulated the Advanced Integral Equation Model for a wide range of soil moisture and surface roughness conditions to characterize the surface roughness effects at different incidence angles. The second is to reduce the effects of vegetation cover on radar measurements by using a semi‐empirical vegetation model and the measurements obtained from the optical sensors (Landsat TM and AVHRR). The vegetation correction was performed based on a first‐order semi‐empirical backscattering vegetation model with the vegetation water content information obtained from the optical sensors as the input. For the validation of this newly developed technique, we compared experimental data obtained from the Southern Great Plain Soil Moisture Experiment in 1997 (SGP97) with our estimations. Comparison with the ground soil moisture measurements showed a good agreement for predication of the relative soil moisture change, in terms of ratio, with a Root Mean Square Error (RMSE) of 1.14. The spatially distributed maps of the relative soil moisture change derived from Radarsat data were also compared with those derived from the airborne passive microwave radiometer ESTAR. The maps of the spatial characteristics of the relative soil moisture change showed comparable results.     

微波遥感监测土壤水分的研究初探

在GPS定位的基础上，同步测量土攘水分、土壤后向散射系数，和同步获取的X波段、HH机化SAR图像进行了土攘水分监N.]的徽波遥感试验研究。结果表明，X波段SAR图像的灰度与表层土壤(0~10cm)水分有较好的相关性，35OHH极化的土峨后向散射系数与SAR图像灰度和土攘水分也有较好的相关性，由SAR图像及土攘的后向散射系数估算的土峨水分精度相近，相对误差均为12%左右，因而利用X波段、HH极化的机载SAR图像监浏土壤水分是可行的。雷达图像的穿透力一般在10cm以内，因此探讨了由表层土壤水分推求剖面土壤水分的可能性，并提出以土攘水分计法在浏童精度和速度上改进传统土壤水分测量的方法。     

土壤湿度微波遥感中的植被散射模型进展

植被是影响土壤湿度微波遥感的主要因子之一,土壤湿度微波遥感的主要任务是建立含有地表土壤信息的植被散射模型。植被散射模型的建立可以加深我们对植被和土壤散射机理的理解,定量分析微波后向散射系数对于各散射因子的敏感性,进一步达到从微波信息中反演土壤湿度的目的。植被散射模型可以分为经验模型、理论模型和半经验模型,各种模型都具有自身的优势和局限性。经验模型的建立比较简单,但一般只适用于特定的研究条件;理论模型是建立在一定的理论基础之上,对于散射因子的考虑相对详尽,但一般模型比较复杂,反演相对困难;半经验模型是前两者的折中,它以植被的宏观物理参量为模型参数,模型的建立和反演比理论模型要简单,但同时也具有一定的理论依据,适用性也较经验模型广。     

使用温度植被干旱指数法(TVDI)反演新疆土壤湿度

利用MODIS合成产品数据MOD11A2和MOD13A2获取的归一化植被指数(NDVI)和陆地表面温度(Ts)构建Ts-NDVI特征空间,依据该特征空间计算的温度植被干旱指数(TVDI)作为土壤湿度监测指标,反演了新疆8、9两个月份每16 d的土壤湿度。使用野外与卫星同步采样的土壤湿度数据进行验证,发现TVDI指标与实测土壤湿度数据显著相关,能够较好地反映表层土壤湿度,反映的新疆土壤湿度的空间分布与新疆的年降水量分布、年平均相对湿度分布很吻合;同时表明8、9两个月份期间新疆土壤湿度低的区域在不断扩大。     

Using remotely-sensed estimates of soil moisture to infer soil texture and hydraulic properties across a semi-arid watershed

Near-surface soil moisture is a critical component of land surface energy and water balance studies encompassing a wide range of disciplines. However, the processes of infiltration, runoff, and evapotranspiration in the vadose zone of the soil are not easy to quantify or predict because of the difficulty in accurately representing soil texture and hydraulic properties in land surface models. This study approaches the problem of parameterizing soil properties from a unique perspective based on components originally developed for operational estimation of soil moisture for mobility assessments. Estimates of near-surface soil moisture derived from passive (L-band) microwave remote sensing were acquired on six dates during the Monsoon '90 experiment in southeastern Arizona, and used to calibrate hydraulic properties in an offline land surface model and infer information on the soil conditions of the region. Specifically, a robust parameter estimation tool (PEST) was used to calibrate the Noah land surface model and run at very high spatial resolution across the Walnut Gulch Experimental Watershed. Errors in simulated versus observed soil moisture were minimized by adjusting the soil texture, which in turn controls the hydraulic properties through the use of pedotransfer functions. By estimating within a continuous range of widely applicable soil properties such as sand, silt, and clay percentages rather than applying rigid soil texture classes, lookup tables, or large parameter sets as in previous studies, the physical accuracy and consistency of the resulting soils could then be assessed.In addition, the sensitivity of this calibration method to the number and timing of microwave retrievals is determined in relation to the temporal patterns in precipitation and soil drying. The resultant soil properties were applied to an extended time period demonstrating the improvement in simulated soil moisture over that using default or county-level soil parameters. The methodology is also applied to an independent case at Walnut Gulch using a new soil moisture product from active (C-band) radar imagery with much lower spatial and temporal resolution. Overall, results demonstrate the potential to gain physically meaningful soil information using simple parameter estimation with few but appropriately timed remote sensing retrievals.     

The application of compact polarimetric decomposition algorithms to L-band PolSAR data in agricultural areas

ABSTRACT

In this paper, the applicability of the recently developed compact polarimetric decomposition and inversion algorithm to estimate soil moisture under low agricultural vegetation cover is investigated using simulated L-band compact polarimetric synthetic aperture radar (PolSAR) data. The surface scattering component is separated from the volume component of the vegetation through a model-based compact polarimetric decomposition (m-α) under the assumption of randomly orientated vegetation volume and reflection symmetry. The extracted surface scattering component is compared with two physics-based, low frequency surface scattering models such as extended Bragg (X-Bragg) and polarimetric two scale model (PTSM) in order to invert soil moisture for corresponding model- and data-derived surface scattering mechanism parameter α_s. In addition to the parameter α_s from m-α decomposition, the applicability of other scattering mechanism parameters, such as δ (relative phase) and χ (degree of circularity) from m-δ and m-χ decompositions are also investigated for their suitability to invert soil moisture. The algorithm is applied on a time series of simulated L-band compact polarimetric E-SAR data from the AgriSAR’2006 campaign over the Görmin test site in Northern Germany. The compact PolSAR-derived soil moisture is validated against in situ time-domain reflectometry (TDR) measurements. Including various growth stages of three different crop types, the estimated soil moisture values indicate an overall root mean square error (RMSE) of 9–12 and 9–15 vol.% using the X-Bragg model and the PTSM, respectively. The inversion rate for vegetation covered soils ranges from 5% to 40% including all phenological stages of the crops and different soil moisture conditions (range from 4 to 34 vol.%). The time series of soil moisture inversion results using compact polarimetry reveal that the developed algorithm is less sensitive to wet soils under growing agriculture crops due to less sensitivity of scattering mechanism parameters α_s and χ for ε_s > 20. Thus, further developments and investigations are needed to invert soil moisture for compact PolSAR data with high inversion rates and consistently less RMSE (<5 vol.%) over the various crop growing season.     

Radar observations of basaltic lava flows,Craters of the Moon,Idaho

Abstract

Radar images were assessed to determine the backscatter characteristics of basaltic lava flows of predominantly pahoehoe textures and the ability to detect fissure vents. The images were obtained from synthetic aperture side-looking airborne radar systems—X-band HH, X-band HV, L-band HH and L-band H V. Smooth, collapsed blisters of shelly pahoehoe have weak returns in all four radar images. These returns are identical to those from pahoehoe surfaces covered with smooth mantles of windblown sediments. Hummocky pahoehoe flows have strong backscatter in all four images, most likely due to the large range in surface roughness causing multiple scattering at both radar wavelengths. Aa lava flows show the greatest variation in backscatter intensities—strong XHH, weak XHV, strong LHH and very strong LHV returns. This variation is due to an increase in multiple scattering at the L-band scale. Although smooth and rough surface textures can be differentiated in the radar images, there are constraints in tracing textural changes back to a particular fissure vent, in part because near-vent flows do not have unique radar signatures. Eruptive fissures are detectable in the radar images by virtue of associated parallel spatter ramparts which have diagnostic, strong backscatter in the X-band images that are in contrast to the weak backscatter of the surrounding shelly pahoehoe lava. However, spatter ramparts are not delineated in the L-band images. The centimetre-scale relief of the agglutinate spatter may cause scattering of the X-band energy more than the L-band energy. Although the structures are several metres high, the look directions for both imaging systems are approximately parallel to the trend of the ramparts. The rampart walls do not serve as reflectors. Such findings emphasize the importance of look direction in the use of radar images to characterize terrains.     

Remote sensing of bare surface soil moisture using EMAC/ESAR data

In this paper we present first results of bare surface soil moisture retrieval using data from the European Multisensor Airborne Campaign/ Experimental Synthetic Aperture Radar (EMAC/ESAR) collected on 9 April 1994 in the Zwalm catchment, Belgium. Data from EMAC Reflective Optics System Imaging Spectrometer (ROSIS) collected on 12 July 1994 over the same catchment were used to develop land use maps. Concurrent to the EMAC/ESAR overflights field data were collected in two subcatchments of the Zwalm catchment. The paper first presents the data processing procedures used for the radar images. Then we apply a theoretical backscattering model to investigate the sensitivity of EMAC/ESAR backscattering coefficients to surface parameters (topography, surface roughness, vegetation and soil moisture). By comparing the predicted backscattering coefficients to the observed ones, we can conclude that classical measurement techniques for surface roughness parameters in remote sensing campaigns are not accurate enough for retrieving soil moisture using theoretical models. A method based on simultaneous retrieval of surface roughness parameters and soil moisture using multiple ESAR measurements is hence proposed. Promising results for retrieved soil moisture confirm the validity of the proposed method.