Dynamic b_p in the L Band and Its Role in Improving the Accuracy of Soil Moisture Retrieval

The parameter b_p in the tuo-omega(τ–ω)model is important for retrieving soil moisture data from passive microwave brightness temperatures.Theoretically,b_p depends on the observation mode(polarization,frequency,and incidence angle)and vegetation properties and varies with vegetation growth.For simplicity,previous studies have taken b_p to be a constant.However,to reduce the uncertainty of soil moisture retrieval further,the present study is of the dynamics of b_p based on the SMAPVEX12 experimental dataset by combining a genetic algorithm and the L-MEB microwave radiative transfer model of vegetated soil.The results show the following.First,b_p decreases nonlinearly with vegetation water content(VWC),decreasing critically when VWC becomes less than 2 kg/m^2.Second,there is a power law between b_p and VWC for both horizontal and vertical polarizations(R^2=0.919 and 0.872,respectively).Third,the effectiveness of this relationship is verified by comparing its soil-moisture inversion accuracy with the previous constant-b_p method based on the HiWATER dataset.Doing so reveals that the dynamic b_p method reduces the root-mean-square error of the retrieved soil moisture by approximately 0.06 cm^3/cm^3,and similar improvement is obtained for the calibrated SMAPVEX12 dataset.Our results indicate that the dynamic b_p method is reasonable for different vegetation growth stages and could improve the accuracy of soil moisture retrieval.

Predicting Surface Roughness and Moisture of Bare Soils Using Multi- band Spectral Reflectance Under Field Conditions

Soil surface roughness, denoted by the root mean square height(RMSH), and soil moisture(SM) are critical factors that affect the accuracy of quantitative remote sensing research due to their combined influence on spectral reflectance(SR). In regards to this issue, three SM levels and four RMSH levels were artificially designed in this study; a total of 12 plots was used, each plot had a size of 3 m × 3 m. Eight spectral observations were conducted from 14 to 30 October 2017 to investigate the correlation between RMSH, SM, and SR. On this basis, 6 commonly used bands of optical satellite sensors were selected in this study, which are red(675 nm), green(555 nm), blue(485 nm), near infrared(845 nm), shortwave infrared 1(1600 nm), and shortwave infrared 2(2200 nm). A negative correlation was found between SR and RMSH, and between SR and SM. The bands with higher coefficient of determination R^2 values were selected for stepwise multiple nonlinear regression analysis. Four characterized bands(i.e., blue, green, near infrared, and shortwave infrared 2) were chosen as the independent variables to estimate SM with R^2 and root mean square error(RMSE) values equal to 0.62 and 2.6%, respectively. Similarly, the four bands(green, red, near infrared, and shortwave infrared 1) were used to estimate RMSH with R^2 and RMSE values equal to 0.48 and 0.69 cm, respectively. These results indicate that the method used is not only suitable for estimating SM but can also be extended to the prediction of RMSH. Finally, the evaluation approach presented in this paper highly restores the real situation of the natural farmland surface on the one hand, and obtains high precision values of SM and RMSH on the other. The method can be further applied to the prediction of farmland SM and RMSH based on satellite and unmanned aerial vehicle(UAV) optical imagery.

An investigation on microwave transmissivity at frequencies of 18.7 and 36.5 GHz for diverse forest types during snow season

Forests have invariably been considered as an obstacle in retrieving land surface parameters from spaceborne passive microwave brightness temperature(T_(B))observations.For quantifying the effect of forests on microwave signals,several models have been developed.However,these models rarely reveal the dependence of microwave radiation on forest types,which can hardly meet the needs of high-accuracy retrieval of terrestrial parameters in forested regions.A ground-based microwave radiometric observation experiment was designed to investigate the dependence of microwave radiation on frequency,polarization,and forest type.Downward TB at 18.7-and 36.5-GHz for horizontal-and vertical-polarization from the forest canopy was measured at 14 sample plots in Northeast China,along with snowpack and forest structural parameters.By providing fits to experimental data,new empirical transmissivity models for three forest types were developed,as a function of woody stem volume and depending on the frequency/polarization.The proposed models give diverse asymptotic transmissivity saturation levels and the corresponding saturation point of woody stem volume for different forest types.Root-mean-square error results between T_(B) simulations and Advanced Microwave Scanning Radiometer-2 observations are approximately 3-6 K.This study provides an experimental and theoretical reference for further development of inversion models for snow parameters in forested areas.