In VHF pulse Ground Penetrating Radar(GPR) system, the echo pass through the antenna and transmission line circuit, then reach the GPR receiver. Thus the reflection coefficient at the receiver sampling gate interface, which is at the end of the transmission line, is different from the real reflection coefficient of the media at the antenna interface, which could cause the GPR receiving error. The pulse GPR receiver is a wideband system that can't be simply described as traditional narrowband transmission line model. Since the GPR transmission circuit is a linear system, the linear transformation method could be used to analyze the characteristic of the GPR receiving system. A GPR receiver calibration method based on transmission line theory is proposed in this paper, which analyzes the relationship between the reflection coefficients of theory calculation at antenna interface and the measuring data by network analyzer at the sampling gate interface. Then the least square method is introduced to calibrate the transfer function of the GPR receiver transmission circuit. This calibration method can be useful in media quantitative inversion by GPR. When the reflection coefficient at the sampling gate is obtained, the real reflection coefficient of the media at the antenna interface can be easily determined.
Fractional vegetation cover (FVC) is an important variable for describing the quality and changes of vegetation in terrestrial ecosystems. Dimidiate pixel models and physical models are widely used to estimate FVC. Six dimidiate pixel models based on different vegetation indices (VI) and four look-up table (LUT) methods were compared to estimate FVC from Landsat 8 OLI data. Comparisons with in situ FVC of steppe and corn showed that the model proposed by Baret et al., which is based on the normalized difference vegetation index (NDVI), predicted FVC most accurately followed by Carlson and Ripley’s method. Gutman and Ignatov’s method overestimated FVC. Modified soil adjusted vegetation index (MSAVI) and the mixture of NDVI and RVI showed potential to replace NDVI in Gutman and Ignatov’s model, whereas the difference vegetation index (DVI) performed less well. At low vegetation cover, the LUT using reflectances to constrain the cost function performed better than LUTs using VI to constrain the cost function, whereas at high vegetation cover, the LUT based on NDVI estimated FVC most accurately. The applications of DVI and MSAVI to constrain the cost function also obtained improvement at high vegetation cover. Overall, the accuracies of LUT methods were a little lower than those of dimidiate pixel models.
Snow depth parameter inversion from passive microwave remote sensing is of great significance to hydrological process and climate systems. The Helsinki University of Technology (HUT) model is a commonly used snow emission model. Snow grain size (SGS) is one of the important input parameters, but SGS is difficult to obtain in broad areas. The time series of SGS are first evolved by an SGS evolution model (Jordan 91) using in situ data. A good linear relationship between the effective SGS in HUT and the evolution SGS was found. Then brightness temperature simulations are performed based on the effective SGS and evolution SGS. The results showed that the biases of the simulated brightness temperatures based on the effective SGS and evolution SGS were −6.5 and −3.6 K, respectively, for 18.7 GHz and −4.2 and −4.0 K for 36.5 GHz. Furthermore, the model is performed in six pixels with different land use/cover type in other areas. The results showed that the simulated brightness temperatures based on the evolution SGS were consistent with those from the satellite. Consequently, evolution SGS appears to be a simple method to obtain an appropriate SGS for the HUT model.
Soil surface temperature (Ts) is an important indicator of global temperature change and a key input parameter for retrieving land surface variables using remote sensing techniques. Due to the masking in the thermal infrared band and the scattering in the microwave band of snow, the temperature of soil surfaces covered by snow is difficult to infer from remote sensing data. We attempted to estimate Ts under snow cover using brightness temperature data from the special sensor microwave/imager. Ts under snow cover was underestimated due to the strong scattering effect of snow on upward soil microwave emissions at 37 GHz. The underestimated portion of Ts is related to snow properties, such as depth, grain size, and moisture. Based on the microwave emission model of layered snowpacks, the simulated results revealed a linear relationship between the underestimated Ts and the brightness temperature difference (TBD) at 19 and 37 GHz. When TBDs at 19 and 37 GHz were introduced to the Ts estimation method, accuracy improved, i.e., the root mean square error and bias of the estimated Ts decreased greatly, especially for dry snow. This improvement allows Ts estimation of snow-covered surfaces from 37 GHz microwave brightness temperature.
The potential of C-band polarimetric synthetic aperture radar data for the discrimination of saline-alkali soils in the western Jilin Province, China, is shown. This area is one of the three saline-alkali landscapes in the world; the presence of saline-alkali soils severely restricts the development of local farming and limits the land use. It is extremely important to identify saline-alkali landscapes accurately and effectively. Radar remote sensing is one of the most promising approaches for saline-alkali soil identification due to the sensitivity of radar data to the dielectric and geometric characteristics of objects, its weather-independent imaging capability, and its potential to acquire subsurface information, independent of the frequency band. Full polarimetric radar data from the RADARSAT-2 satellite were used. We focused on target decomposition theory and the statistical classification approach using a Wishart distribution to identify saline-alkali soils. The precise validation of the classification results is based on 129 ground sampling points. The results indicate that the polarimetric classifications using the H-α¯ method performed poorly, with Kappa values of approximately 0.29. The classification method based on Freeman-Durden decomposition showed better results, with Kappa values of approximately 0.54 and an overall accuracy of 68.22%. The best result was achieved using an input of anisotropy, with Kappa values of approximately 0.62 and an overall accuracy of 74.42%. The validity of the anisotropy approach implies that the scattering randomness of saline-alkali soil is very strong, which reflects the complex scattering characteristics of saline-alkali landscapes. Further study of the scattering characteristics of saline-alkali soil is necessary.
Forest covers about 30% of earth surface, which plays an important role in global forecast and carbon cycle.
Monitoring forest biomass, and retrieving soil moisture at forest area, are the main goals of most passive microwave
sensors on satellite missions. L-band is the most sensitive frequency among all the frequencies due to its good penetration
ability. Because of its variety of the size of scattering components, the complicated structures and species of forest, it is
difficult to describe the scattering and attenuation characters of forest in modeling microwave emission at forest area.
In this paper, we studied the emissivity and transmissivity of deciduous forest at L(1.4GHz) by model simulation and
field experiment. The microwave emission model was based on Matrix-Doubling algorithm. The comparison between
simulated emissivity and measured data collected during an experiment at Maryland, USA in 2007 was good.
Since theoretical model like Matrix-Doubling is too complicated to be used in retrial application, we mapped the
results of Matrix-Doubling to a simple 0th-order model, also called ω-τ model, by setting the simulated emissivity to be the
emissivity of 0th-order model at the same environment, which 2 unknown variables---opacity τ and effective single
scattering albedo ω need to be determined.
To valited τ (transmissivity of forest) simulated by Matrix-Doubling, we took an deciduous forest experiment by an L
band microwave radiometer under trees at JingYueTan area, Changchun, Jilin Province in April to June in 2014. Thus the ω
of forest can be determined.
The matching results are presented in this paper. The relationship between LAI and forest microwave characters are
discussed.
To improve snow depth (SD) inversion algorithms using passive microwave data, it is important to objectively assess their accuracy and to analyze their uncertainty. Some previous studies validated the inversion algorithms only using spatial data at a fixed time node, which is not objective or convincing. The spatiotemporal analysis of the SD inversion based on the FengYun-3B MicroWave Radiation Imager is performed in Heilongjiang Province, China. Based on the temporal analysis, the results show that the accuracy of SD inversion algorithms is different at different time phases throughout the winter. In cropland areas, the variation in snow properties, particularly the increase in snow grain and the presence of depth hoar, leads to underestimation and overestimation at the earlier and later phases, respectively. The spatial analysis shows that the SD in the high forest coverage regions is seriously overestimated due to the addition of a forest correction factor using the Chang algorithm. In addition, the complex underlying surfaces and hilly terrain are also influencing factors that result in the low accuracy for several regions. Therefore, the analysis and identification of these uncertainties are benefits not only in understanding the influential factors of SD inversion algorithms but also in developing better algorithms for the next generation of SD retrieval.
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