Retrieval Of Surface Parameters Research Articles

SEVIRI Hyper-Fast Forward Model with Application to Emissivity Retrieval.

Timely processing of observations from multi-spectral imagers, such as SEVIRI (Spinning Enhanced Visible and Infrared Imager), largely depends on fast radiative transfer calculations. This paper mostly concerns the development and implementation of a new forward model for SEVIRI to be applied to real time processing of infrared radiances. The new radiative transfer model improves computational time by a factor of ≈7 compared to the previous versions and makes it possible to process SEVIRI data at nearly real time. The new forward model has been applied for the retrieval of surface parameters. Although the scheme can be applied for the simultaneous retrieval of temperature and emissivity, the paper mostly focuses on emissivity. The inverse scheme relies on a Kalman filter approach, which allows us to exploit a sequential processing of SEVIRI observations. Based on the new forward model, the paper also presents a validation retrieval performed with in situ observations acquired during a field experiment carried out in 2017 at Gobabeb (Namib desert) validation station. Furthermore, a comparison with IASI (Infrared Atmospheric Sounder Interferometer) emissivity retrievals has been performed as well. It has been found that the retrieved emissivities are in good agreement with each other and with in situ observations, i.e., average differences are generally well below 0.01.

Optimization approach to retrieve soil surface parameters from single-acquisition single-configuration SAR data

This study suggests a novel approach to the retrieval of soil surface parameters using a single-acquisition single-configuration synthetic-aperture radar (SAR) system. Soil surface parameters such as soil moisture and surface roughness are key elements for many environmental studies, including Earth surface water cycles, energy exchange, agriculture, and geology. Remote sensing techniques, especially SAR data, are commonly used to retrieve such soil surface parameters over large areas. Several backscattering models have been proposed for soil surface parameters retrieval from SAR data. However, commonly, these backscattering models require multi configuration SAR data, including multi-polarization, multi-frequency, and multi-incidence angle. Here we propose a methodology that employs single-acquisition single-configuration SAR data for the retrieval of soil surface parameters. The originality is to use single-acquisition single-configuration SAR data to retrieve the soil surface parameters using an optimization approach by the genetic algorithm (GA); we have used the modified Dubois model (MDM) in HH polarization as the backscattering model. Three HH polarization and C band data sets from Quebec (Radarsat-1), Ontario (SIR-C), and Oklahoma (AIRSAR) were analyzed. The retrieved values of soil moisture and soil surface roughness were then compared to ground truth measurements with corresponding parameters. We employed diverse criteria, including the mean absolute error (MAE), the root mean square error (RMSE), the coefficient of performance (CP), and the correlation coefficient to investigate the performance of the proposed methodology. This analysis suggests the capability of the GA for the retrieval of soil surface parameters. Based on our findings, this method presents a viable alternative approach to the retrieval of soil surface parameters when only single-acquisition single-configuration SAR data is available.

Soil Moisture Retrieval of Winter Wheat Fields Based on Random Forest Regression Using Quad-Polarimetric SAR Images

Soil moisture has great significance in the researches of hydrology, meteorology and agriculture yield estimation. The quad-polarimetric SAR images can provide a lot of polarimetric features, the significance of the features in surface parameter retrieval have attracted attentions in previous researches with no final conclusions because of the complexity of terrain scattering. In this paper, random forest regression (RFR) is used for both soil moisture retrieval and the importance evaluation of polarimetric features of Radarsat-2 images in winter wheat fields. According to the score of importance, feature selection and combination are done for modelling. We evaluate the retrieval accuracy of models with different feature combinations. The results show that models of important features selected by RFR have RMSE(root mean square error) less than 6% which are better results compared to traditional models; when compared with support vector regression and artifical neural networks, the RFR also shows best retrieval accuracies, which proves that RFR is suitable for soil moisture retrieval and feature selection. The high retrieval accuracies of LBC-CPD(linear backscatter coefficients-Cloude-Pottier decomposition) and LBC-CPR(linear backscatter coefficients-circular polarimetric ratio) indicates these features can improve the retrieval accuracy of soil moisture.

Denoising vegetation spectra by combining mathematical-morphology and wavelet-transform-based filters

Spectral noise causes distorted spectra, shifting the central wavelength and thus reducing the accuracy of surface parameter retrieval. A hybrid method combining mathematical-morphology and wavelet-transform (WB)-based filters was used to remove spectral noise. First, a generalized mathematical-morphology (GM) filter was used to remove large-amplitude noise, and then the processed spectra were smoothed using the WT-based filter to remove small-amplitude noise. The simulated noise spectrum and 76 measured canopy spectra for winter wheat were denoised with three filters: the combination filter (CF), GM, and WT. In the simulated experiments, five evaluation indices were calculated to evaluate the denoising effects. For measured spectra, qualitative analyses were performed based on spectral characteristics. Quantitative evaluations were conducted by deriving various vegetation indices from denoised spectra to retrieve wheat’s biophysical and biochemical parameters. The results indicated that the CF removed both large- and small-amplitude noise efficiently, improving signal-to-noise ratio and peak signal-to-noise ratio of simulated noise spectrum and retrieval accuracy of leaf water content (LWC) significantly. Meanwhile, it better maintained the waveform and smoothness of spectrum, improving the retrieval accuracies of leaf area index and chlorophyll data slightly. The coefficient of determination (R2) of developed model between the modified normalized difference water index and LWC was improved from 0.428 to 0.622 using the CF, 0.555 using the GM, and 0.549 using the WT. The R2 and root mean square error between the measured and retrieval LWC were improved from 0.364 and 0.027 to 0.611 and 0.018 using the CF, whereas the corresponding values were 0.504 and 0.022 for the GM, and 0.478 and 0.023 for the WT.

Optimal estimation retrieval for directional polarimetric camera onboard Chinese Gaofen-5 satellite: an analysis on multi-angle dependence and a posteriori error

Data from the directional polarimetric camera (DPC) instrument onboard Chinese Gaofen-5 satellite dedicated to aerosol monitoring have been available recently. By measuring the spectral, angular and polarization properties of the radiance at the top of atmosphere (TOA), a DPC provides the aerosol optical depths (AODs) as well as partial microphysical aerosol properties. In order to evaluate the capability and the retrieval uncertainty of DPC sensor systematically, the information content and a posteriori error analysis are applied to the synthetic data of DPC multi-angle observation in this paper, which inherits from the optimal estimate theoretical framework. The forward simulation is conducted by the unified linearized vector radiative transfer model (UNL-VRTM), and the Jacobians of four Stokes elements with respect to aerosol and surface model parameters can be obtained simultaneously. Firstly, the error influences of surface parameter on the TOA measurements are simulated. The results indicate that a 10% relative error of parameter <i>k</i><sub>1</sub> in the improved BRDF model results in about 4.65% error of the TOA reflectance, while the error of TOA polarized reflectance caused by the same error of parameter <i>C</i> in BPDF model is negligibly small. Secondly, the multi-angle dependence of total information content in DPC measurements is investigated. It is shown that the information content increases significantly with the number of viewing angles, especially for the measurements of the first 9 angles. The DPC multi-angle observation can provide extra 5 degrees of freedom for signal (DFS) for the retrieval of aerosol and surface parameters, in which the retrieval of aerosol parameters is more sensitive to observation geometries than the retrieval of surface parameters in most cases. In addition, the total aerosol DFS increases with the range extension of scattering angle under the same number of viewing angles. After that, the DFS of each retrieved aerosol and surface parameter are given. For the aerosols, the volume concentration, real-part refractive index and effective radius show a high DFS (greater than 0.8). For the surfaces, the mean DFS of each parameter is greater than 0.5, which indicates the well capability of DPC in the surface retrieval. Finally, the a posteriori error of each aerosol, surface parameter and corresponding vary with the number of viewing angles, and the observation error and aerosol model error are discussed. The a posteriori error decrease significantly with the number of viewing angles, and the influence of the aerosol model error on the a posteriori error is not remarkable. In general, the observation error is the main influence factor on the uncertainty of the inversion results.

Analysis and Simulation on Imaging Performance of Backward and Forward Bistatic Synthetic Aperture Radar

In recent years, bistatic synthetic aperture radar (SAR) technique has attracted considerable and increasing attention. Compared to monostatic SAR for which only the backscattering is measured, bistatic SAR expands the scattering measurements in aspects of angular region and polarization, and greatly enhances the capability of remote sensing over terrain and sea. It has been pointed out in recent theoretical researches that bistatic scattering measured in the forward region is preferable to that measured in the backward region in lines of surface parameters retrieval. In the forward region, both dynamic range and signal sensitivity increase to a great extent. For these reasons, bistatic SAR imaging is desirable. However, because of the separated positions of the transmitter and receiver, the degrees of freedom in the parameter space is increased and the forward bistatic imaging is more complicated than the backward bistatic SAR in the aspects of bistatic range history, Doppler parameter estimation and motion compensation, et, al. In this study, we analyze bistatic SAR in terms of ground range resolution, azimuth resolution, bistatic range history and signal to noise ratio (SNR) in different bistatic configurations. Effects of system motion parameters on bistatic SAR imaging are investigated through analytical modeling and numerical simulations. The results indicate that the range resolution is extremely degraded in some cases in forward bistatic SAR imaging. In addition, due to the different imaging projection rules between backward and forward bistatic SAR, the ghost point is produced in the forward imaging. To avoid the above problems, the forward bistatic imaging geometry must be carefully considered. For a given application requirement with the desired imaging performances, the design of the motion parameters can be considered as a question of solving the nonlinear equation system (NES). Then the improved chaos particle swarm optimization (CPSO) is introduced to solve the NES and obtain the optimal solutions. And the simulated imaging results are used to test and verify the effectiveness of CPSO. The results help to deepen understanding of the constraints and properties of bistatic SAR imaging and provide the reference to the optimal design of the motion parameters for a specific requirement, especially in forward bistatic configurations.

Directional Polarimetric Camera (DPC): Monitoring aerosol spectral optical properties over land from satellite observation

The Directional Polarimetric Camera (DPC) is the first Chinese multi-angle polarized earth observation satellite sensor, which will be launched onboard the GaoFen-5 Satellite in Chinese High-resolution Earth Observation Program. GaoFen-5 runs in a sun-synchronous orbit with an inclination angle of 98°, with the 13:30 PM local overpass time and the 2-days revisiting period. DPC employed a charge coupled device detection unit with 512 × 512 effective pixels from the 544 × 512 useful pixels, realizing spatial resolution of 3.3 km under a swath width of 1850 km. Meanwhile, DPC has 3 polarized channels (490, 670 and 865 nm) together with 5 non-polarized bands (443, 565, 763, 765 and 910 nm), and can obtain at least 9 viewing angles by continuously capturing series images over the same target on orbit. Based on the optimal estimation theory and improved bidirectional reflectance distribution function model, an inversion framework for the simultaneous retrieval of aerosol and surface parameters is presented by taking full advantage of available radiometric and polarimetric measurements. The retrieved wavelength-independent fine-mode and coarse-mode aerosol volumes are used to assess the DPC performance on the inversion capability of spectral aerosol optical depth (AOD), from which the Angstrom exponent and fine-mode fraction (FMF) could be further obtained. In addition, based on the synthetic DPC data for various observation geometries, aerosols and surface types, the aerosol inversion capabilities are systematically evaluated, and the information content analysis results show that the aerosol spectral optical properties can be well retrieved over various land surfaces.

Global Land Cover Heterogeneity Characteristics at Moderate Resolution for Mixed Pixel Modeling and Inversion

Spatial heterogeneity is present in the land surface at every scale and is one of the key factors that introduces inherent uncertainty into simulations of land surface processes and parameter retrieval based on remotely sensed data. Because of a lack of understanding of the heterogeneous characteristics of global mixed pixels, few studies have focused on modeling and inversion algorithms in heterogeneous areas. This paper presents a parameterization scheme to describe land cover heterogeneity quantitatively by composition and boundary information based on high-resolution land cover products. Global heterogeneity features at the 1-km scale are extracted from the ‘GlobeLand30’ land cover dataset with a spatial resolution of 30 m. The composition analysis of global mixed pixels shows that only 35% of pixels over the land surface of Earth are covered by a single land cover type, namely, pure pixels, and only 25.8% are located in vegetated areas. Pixels mixed with water are more common than pixels mixed with any other non-vegetation type. The fragmentation analysis of typical biomes based on the boundary length shows that the savanna is the most heterogeneous biome, while the evergreen broadleaf forest is the least heterogeneous. Deciduous needleleaf forests are significantly affected by canopy height differences, while crop and grass biomes are less affected. Lastly, the strengths and limitations of the method and the application of the land cover heterogeneity characteristics extracted in this study are discussed.

Deriving high-quality surface emissivity spectra from atmospheric infrared sounder data using cumulative distribution function matching and principal component analysis regression

The Atmospheric Infrared Sounder (AIRS) provides limited hyperspectral thermal infrared (TIR) emissivity data for the retrieval of critical land surface and climate parameters in environmental research. However, the AIRS land surface emissivity (LSE) data lack accuracy, resulting in low-quality data retrieval, particularly for the lower boundary layer. In this study, a practical and effective method is proposed to derive high-accuracy AIRS LSE data and continuous emissivity spectra in the TIR range of 8–14.5 μm. The AIRS LSE is first rescaled to the Moderate Resolution Imaging Spectroradiometer (MODIS) LSE using cumulative distribution function (CDF) matching, and then the emissivity spectra are recovered from the rescaled AIRS LSE using principal component analysis (PCA) regression. The results show that rescaling the AIRS LSE significantly reduced the bias and root mean square (RMS) error in the study area of Africa and the Arabian Peninsula, and PCA regression successfully recovered the emissivity spectra in the 8–14.5 μm range from the rescaled AIRS LSE. At two validation sites in the Namib and Kalahari deserts of southern Africa, the biases of the rescaled AIRS LSE at three hinge points are 0.62% and 0.61%, respectively, and the biases of the recovered AIRS LSE spectra in the 8–12 μm TIR range are 0.53% and 0.56%, respectively. Variations in land cover homogeneity and the accuracy of the MODIS LSE are the critical factors impacting the final accuracy of the rescaled AIRS LSE and the recovered emissivity spectra.

Estimation of Microwave Atmospheric Transmittance Over China

Atmospheric transmittance is an important factor for atmospheric correction in the inversion of land surface parameters. Under nonprecipitating conditions, microwave atmospheric transmittance in the X-, Ku-, and Ka-bands is mainly determined by oxygen, water vapor, and cloud liquid water content. In this letter, radiosoundings from 119 stations in China, performed twice a day from January 2011 to July 2014, were used in the Salonen–Uppala cloud detection algorithm to distinguish cloud layers from layered atmospheric profiles and to estimate the cloud liquid water content therein. The resulting atmospheric transmittances at frequencies of the Advanced Microwave Scanning Radiometer—Earth Observing System over China were estimated using Liebe’s millimeter-wave propagation model and Mie theory. Atmospheric transmittance maps were obtained by interpolating the results from individual sites, driving a climatological database for over China, which may be used to correct for atmospheric influence in surface parameter retrievals. The simulated transmittances were validated through a series of on-site field experiments in the North of China. We compared simulated atmospheric brightness temperature with measurements performed in situ using a ground-based radiometer system. The correlation coefficients between the measured and simulated values were 0.97, 0.99, and 0.98, in the X-, Ku-, and Ka-bands, respectively, with a root-mean-square error of 0.6, 1.6, and 9.5 K, respectively.

Full-Wave Simulation and Analysis of Bistatic Scattering and Polarimetric Emissions From Double-Layered Sastrugi Surfaces

In this paper, a physically based numerical electromagnetic approach, by solving Maxwell's equations, is developed to investigate the scattering and the emission from double-layered media, where both the top and bottom interfaces are random sastrugi surfaces with a random horizontal shift between the two corresponding negative-slope facets of both interfaces. Numerical simulations are illustrated for bistatic scattering and four Stokes parameters at L-, C-, X-, and K-bands. Results indicate that the bistatic scattering and Stokes parameters are asymmetric for the double-sastrugi media, whereas for the other two structures, sastrugi surface alone and sastrugi surface with a planar bottom boundary, their scattering and the first two Stokes parameters are even symmetric relative to the azimuthal angle of 90°, and the third and fourth Stokes parameters are odd symmetric. In particular, the Stokes results no longer have the strong coherent fluctuations in angular variations shown in periodic double-layered structures because the random double-layered structure eliminates the coherent interference. It is interesting to observe that the internal total reflection may cancel if the coupled interactions between the two sastrugi interfaces are very strong, which results in decreasing scattering at the L-band, and its Stokes parameters are similar to those of the sastrugi alone. Numerical results also reveal that the maxima of cross-polarized specular scattering from double-sastrugi structures can be observed at cross incidence; however, they are at a deep dip for the latter two statistical symmetric structures. The sastrugi-sastrugi structure, being geometrically anisotropic, is capable of generating strong cross-polarized scattering and, subsequently, significant amounts of the third and fourth Stokes. The azimuthal patterns, at a viewing angle of 55°, of four Stokes parameters, although more complex, are feature rich where two striking extrema of third and fourth Stokes are presented. Compared with the L-band, the C-band presents strong azimuthal dependence of four Stokes. The simulation results offer deeper insights into the scattering and emission process in sastrugi surface and may lead to better retrieval of surface parameters from radar or radiometric measurements.

Noncontact three-dimensional quantitative profiling of fast aspheric lenses by optical coherence tomography

Abstract The use of optical coherence tomography (OCT) for noncontact three-dimensional aspheric lens profiling and retrieval of aspheric surface parameters is demonstrated. Two commercially available aspheric lenses with different focal length-to-diameter ratio have been imaged using OCT, and the measured optical path length distribution has been least square fitted with the aspheric lens surface retrieving the radius of curvature, aspheric constant, and conic constants. The refractive index of these lenses has also been measured referencing with a standard Zerodur glass flat. The fitted aspheric surface coefficients of the lenses are in close agreement with the manufacturer’s values, thus, envisaging the potential of OCT in rapid screening, testing of aspheric lenses, and other micro-optical components such as those used in illumination optics.

Sea surface imaging simulation and sea wave retrieval method \\based on MIMO radar

Multiple-input-multiple-output (MIMO) radar is an emerging radar system that has significant potential. MIMO radar can provide a higher resolution real-time imaging solution than traditional phased-array radar schemes and is well suited for the imaging of complex sea surfaces. MIMO radar imaging of sea scenes is still in the exploratory stage and several key issues need to be resolved and improved further. With the objective of achieving sea surface monitoring using MIMO radar, some basic research work on MIMO radar imaging simulation of sea surfaces, and MIMO radar sea current and wave information retrieval were conducted in this study. In order to analyze the feasibility and advantages inherent in MIMO radar imaging simulation of sea surfaces, ocean surface imaging based on MIMO radar was carried out via computer simulation. The curvature-modified two-scale method and shadowing modulation were used to build a back scatter section distribution model of a sea surface, which is taken as the object of MIMO radar imaging. In terms of sea current and wave information retrieval from MIMO radar images, the weighted least square method was used to fully explore the advantages of MIMO radar surface imaging. While retrieving the wave spectrum and wave parameters, the modulation transfer function of MIMO radar was determined via imaging simulation experiments. The retrieved significant wave height, main wave direction, and cross-zero cycle from MIMO radar images were found to be more precise than those from navigation radar images. The simulation results obtained also showed that MIMO radar sea surface imaging and parameter retrieval are superior to and more effective than marine radar.

Simultaneous physical retrieval of Martian geophysical parameters using Thermal Emission Spectrometer spectra: the φ-MARS algorithm.

In this paper, we present a new methodology for the simultaneous retrieval of surface and atmospheric parameters of Mars. The methodology is essentially based on similar codes implemented for high-resolution instruments looking at Earth, supported by a statistical retrieval procedure used to initialize the physical retrieval algorithm with a reliable first guess of the atmospheric parameters. The methodology has been customized for the Thermal Emission Spectrometer (TES), which is a low-resolution interferometer. However, with minor changes to the forward and inverse modules, it is applicable to any instrument looking at Mars, and with particular effectiveness to high-resolution instruments. The forward module is a monochromatic radiative transfer model with the capability to calculate analytical Jacobians of any desired geophysical parameter. In the present work, we describe the general methodology and its application to a large sample of TES spectra. Results are drawn for the case of surface temperature and emissivity, atmospheric temperature profile, water vapor, and dust and ice mixing ratios. Comparison with climate models and other TES data analyses show very good agreement and consistency.

Relationship Between Forest Microwave Transmissivity and Structural Parameters for the Canadian Boreal Forest

This letter proposes relationships between boreal forest microwave transmissivity and four forest structural parameters: summer and winter Leaf Area Index (LAI) from MODIS, biomass $(\hbox{t ha}^{-1})$ , and total volume $(\hbox{m}^{3} \hbox{ha}^{-1})$ for northern Quebec, Canada. These relationships were derived for summer AMSR-E data sets that took into account the effects of canopy emission and scattering. Root mean square error results between brightness temperature simulations and ASMR-E observations are approximately 5 K. Results reported in this study can be used as forest correction equations for key surface parameter retrievals under the boreal forest canopy, such as soil moisture or snow depth/water equivalent.

Improvements in farmland surface roughness measurement by employing a new laser scanner

Farmland surface roughness is related to the surface root mean square height (rmsh) and surface correlation length (cl), and it is not only an important factor influencing wind and water erosion but also an input parameter for modeling radar backscattering coefficients and the passive microwave brightness temperature. Although these surface roughness parameters are important for soil erosion estimation and land surface parameter retrieval from microwave remote sensing data, their measurement accuracy is limited by the measurement method, instruments used and data preprocessing. To obtain high-precision surface roughness measurements, a laser scanner with spatial measuring range of 400mm×600mm is used to measure the surface height of four test sites with different surface roughness. Based on the surface height data measured using the laser scanner, the effect of segment number on the surface roughness accuracy is analyzed using the random sampling method. The results show that the measurement uncertainties rmshm and clm decrease with increasing segment number. Among all four test sites, 80% and 90% accuracies can be achieved for rmshm and clm, respectively. In addition, the required segment number for the same clm accuracy is found to be related to the degree of surface roughness, but this conclusion does not hold for rmshm. In summary, the accuracy of the surface roughness measured by the laser scanner is greater than 80%, and this scanner is very suitable for field measurements due to its ease of operability and mobility.

A physical approach for a simultaneous retrieval of sounding, surface, hydrometeor, and cryospheric parameters from SNPP/ATMS

AbstractWe present in this study the results obtained when applying a physical algorithm based on a variational methodology to data from the Advanced Technology Microwave Sounder (ATMS) onboard the Suomi National Polar‐Orbiting Partnership (SNPP) for a consistent retrieval of geophysical data in all weather conditions. The algorithm, which runs operationally at the U.S. National Oceanic and Atmospheric Administration, is applied routinely to a number of sounders from the Polar‐Orbiting Operational Environmental Satellites, the Defense Meteorological Satellite Program, and the European Meteorological Operational satellite constellations. The one‐dimension variational (1DVAR) methodology, which relies on a forward operator, the Community Radiative Transfer Model, allows for solving the inversion of the radiometric measurements into geophysical parameters which have a direct impact on the brightness temperatures. The parameters that are produced by this Microwave Integrated Retrieval System algorithm include the atmospheric temperature T(p), moisture Q(p), and vertically integrated total precipitable water; and the surface skin temperature and emissivity as well as the hydrometeor products of nonprecipitating cloud liquid water and rain‐ and ice‐water paths. In this algorithm, a simple postprocessing is applied to the 1DVAR‐generated emissivity to derive cryospheric products (snow water equivalent and sea‐ice concentration) when the data are measured over these surfaces. The postprocessing is also applied to the hydrometeors products to generate a surface rainfall rate. This comprehensive set of sounding, surface, hydrometeor, and cryospheric products generated from SNPP/ATMS is therefore radiometrically consistent, meaning that when input to the forward operator, it will allow the simulation of the actual brightness temperatures measurements within noise levels. The geophysical consistency between the products, also critical, is satisfied due to the physical approach adopted and the geophysical constraints introduced through the correlation matrix used in the variational system. The results shown in this paper confirm that the performances of all products are within the expected accuracy and precision figures and comparable to performances usually obtained with single‐parameter‐dedicated algorithms, with the added value that the inverted products are both radiometrically and geophysically consistent.

Investigating the Relationship between X-Band SAR Data from COSMO-SkyMed Satellite and NDVI for LAI Detection

Monitoring spatial and temporal variability of vegetation is important to manage land and water resources, with significant impact on the sustainability of modern agriculture. Cloud cover noticeably reduces the temporal resolution of retrievals based on optical data. COSMO-SkyMed (the new Italian Synthetic Aperture RADAR-SAR) opened new opportunities to develop agro-hydrological applications. Indeed, it represents a valuable source of data for operational use, due to the high spatial and temporal resolutions. Although X-band is not the most suitable to model agricultural and hydrological processes, an assessment of vegetation development can be achieved combing optical vegetation indices (VIs) and SAR backscattering data. In this paper, a correlation analysis has been performed between the crossed horizontal-vertical (HV) backscattering (s°HV) and optical VIs (VIopt) on several plots. The correlation analysis was based on incidence angle, spatial resolution and polarization mode. Results have shown that temporal changes of s°HV (Δs°HV) acquired with high angles (off nadir angle; θ > 40°) best correlates with variations of VIopt (ΔVI). The correlation between ΔVI and Δs°HV has been shown to be temporally robust. Based on this experimental evidence, a model to infer a VI from s° (VISAR) at the time, ti + 1, once known, the VIopt at a reference time, ti, and Δs°HV between times, ti + 1 and ti, was implemented and verified. This approach has led to the development and validation of an algorithm for coupling a VIopt derived from DEIMOS-1 images and s°HV. The study was carried out over the Sele plain (Campania, Italy), which is mainly characterized by herbaceous crops. In situ measurements included leaf area index (LAI), which were collected weekly between August and September 2011 in 25 sites, simultaneously to COSMO-SkyMed (CSK) and DEIMOS-1 imaging. Results confirm that VISAR obtained using the combined model is able to increase the feasibility of operational satellite-based products for supporting agricultural practices. This study is carried out in the framework of the COSMOLAND project (Use of COSMO-SkyMed SAR data for LAND cover classification and surface parameters retrieval over agricultural sites) funded by the Italian Space Agency (ASI).

Sensitivity of C-band SAR polarimetric variables to unvegetated agricultural fields

The interaction of linear polarized microwaves with agricultural features is well understood. Much less is understood about polarimetric data and the potential use of these data to improve surface parameter retrieval models. This paper explores the soil surface information provided by quad-polarimetric SAR through a review of previous work and an empirical sensitivity study of RADARSAT-2 data at four incidence angles. Soil moisture, surface roughness, and crop residue data are quantitatively sampled in unvegetated fields. RADARSAT-2 variables include: linear backscatter and polarization ratios; copolarized phase difference and magnitude of the copolarized complex correlation; pedestal height; extrema of the scattered intensity, completely polarized, and completely unpolarized components; and parameters of the Cloude–Pottier scattering decomposition. Results demonstrated that sensitivities of field averaged linear backscatter were reproduced from previous reports, lending confidence to the experiment. Field averaged pedestal height and copolarized complex correlation coefficient showed significant relationships to crop residue and surface roughness, suggesting an ability to characterize volume and multiple scattering. Similarly, dynamic range of the degree of polarization showed significant relationships with crop residue cover at higher incidence angles. Target averaged or variance of copolarized phase difference did not produce a consistent relationship with the surface parameters, in contrast to qualitative based suggestions of previous experiments. Extremas of the scattered intensity and completely polarized components indicated comparable relationships to surface features as the like polarized linear intensity channels, suggesting sensitivities of these variables to surface scattering. The extrema of the completely unpolarized component showed comparable relationships to surface features as the pedestal height. Results of this paper contribute to identification of optimal SAR variables for use in agricultural monitoring and evaluate potential contributions of polarimetric data for improving surface parameter retrieval models.

Evaluation of precipitation detection over various surfaces from passive microwave imagers and sounders

During the middle part of this decade, a wide variety of passive microwave imagers and sounders will be unified in the Global Precipitation Measurement (GPM) mission to provide a common basis for frequent (3h) global precipitation monitoring. The ability of these sensors to detect precipitation by discerning it from non-precipitating background depends upon the channels available and characteristics of the surface and atmosphere. This study quantifies the minimum detectable precipitation rate and fraction of precipitation detected for four representative instruments (TMI, GMI, AMSU-A, and AMSU-B) that will be part of the GPM constellation. Observations for these instruments were constructed from equivalent channels on the SSMIS instrument on DMSP satellites F16 and F17 and matched to precipitation data from NOAA's National Mosaic and QPE (NMQ) during 2009 over the continuous United States. A variational optimal estimation retrieval of non-precipitation surface and atmosphere parameters was used to determine the consistency between the observed brightness temperatures and these parameters, with high cost function values shown to be related to precipitation.The minimum detectable precipitation rate, defined as the lowest rate for which probability of detection exceeds 50%, and the detected fraction of precipitation are reported for each sensor, surface type (ocean, coast, bare land, snow cover) and precipitation type (rain, mix, snow). The best sensors over ocean and bare land were GMI (0.22mmh−1 minimum threshold and 90% of precipitation detected) and AMSU (0.26mmh−1 minimum threshold and 81% of precipitation detected), respectively. Over coasts (0.74mmh−1 threshold and 12% detected) and snow-covered surfaces (0.44mmh−1 threshold and 23% detected), AMSU again performed best but with much lower detection skill, whereas TMI had no skill over these surfaces. The sounders (particularly over water) benefited from the use of re-analysis data (vs. climatology) to set the a priori atmospheric state and all instruments benefited from the use of a conditional snow cover emissivity database over land. It is recommended that real-time sources of these data be used in the operational GPM precipitation algorithms.