Long Liu

Rice growth monitoring using simulated compact polarimetric C band SAR

In this study, a set of nine compact polarimetric (CP) images were simulated from polarimetric RADARSAT-2 data acquired over a test site containing two types of rice field in Jiangsu province, China. The types of rice field in the test site were (1) transplanted hybrid rice fields, and (2) direct-sown japonica rice fields. Both types have different yields and phenological stages. As a first step, the two types of rice field were distinguished with 94% and 86% accuracy respectively through analyzing CP synthetic aperture radar (SAR) observations and their behavior in terms of scattering mechanisms during the rice growth season. The focus was then on phenology retrieval for each type of rice field. A decision tree (DT) algorithm was built to fulfill the precise retrieval of rice phenological stages, in which seven phenological stages were discriminated. The key criterion for each phenological stage was composed of 1–4 CP parameters, some of which were first used for rice phenology retrieval and found to be very sensitive to rice phenological changes. The retrieval results were verified at parcel level for a set of 12 stands of rice and up to nine observation dates per stand. This gave an accuracy of 88–95%. Throughout the phenology retrieval process, only simulated CP data were used, without any auxiliary data. These results demonstrate the potential of CP SAR for rice growth monitoring applications.

Scattering Mechanisms for the "Ear" Feature of Lop Nur Lake Basin

Lop Nur is a famous dry lake in the arid region of China. It was an important section of the ancient “Silk Road”, famous in history as the prosperous communication channel between Eastern and Western cultures. At present, there is no surface water in Lop Nur Lake basin, and on SAR (Synthetic Aperture Radar) images, it looks like an “Ear”. The objective of this paper is to interpret the Lop Nur phenomenon from the perspective of scattering mechanisms. Based on field investigation and analysis of sample properties, a two-layer scattering structure is proposed with detailed explanations of scattering mechanisms. In view of the rough surface, the MIEM (Modified Integral Equation Model) was introduced to represent air-surface scattering in Lop Nur. Then, a two-layer scattering model was developed which can describe surface scattering contribution. Using polarimetric decomposition, validations were carried out, and the RMSE (root mean square error) values for the HH and VV polarizations were found to be 1.67 dB and 1.06 dB, respectively. Furthermore, according to model parametric analysis, surface roughness was identified as an apparent reason for the “Ear” feature. In addition, the polarimetric decomposition result also showed that the volume scattering part had rich texture information and could portray the “Ear” feature exactly compared with the other two parts. It is maintained that subsurface properties, mainly generating volume scattering, can determine the surface roughness under the certain climate conditions, according to geomorphological dynamics, which can help to develop an inversion technology for Lop Nur.

Modeling and Mapping Soil Moisture of Plateau Pasture Using RADARSAT-2 Imagery

Accurate soil moisture retrieval of a large area in high resolution is significant for plateau pasture. The object of this paper is to investigate the estimation of volumetric soil moisture in vegetated areas of plateau pasture using fully polarimetric C-band RADARSAT-2 SAR (Synthetic Aperture Radar) images. Based on the water cloud model, Chen model, and Dubois model, we proposed two developed algorithms for soil moisture retrieval and validated their performance using experimental data. We eliminated the effect of vegetation cover by using the water cloud model and minimized the effect of soil surface roughness by solving the Dubois equations. Two experimental campaigns were conducted in the Qinghai Lake watershed, northeastern Tibetan Plateau in September 2012 and May 2013, respectively, with simultaneous satellite overpass. Compared with the developed Chen model, the predicted soil moisture given by the developed Dubois model agreed better with field measurements in terms of accuracy and stability. The RMSE, R2, and RPD value of the developed Dubois model were (5.4, 0.8, 1.6) and (3.05, 0.78, 1.74) for the two experiments, respectively. Validation results indicated that the developed Dubois model, needing a minimum of prior information, satisfied the requirement for soil moisture inversion in the study region.

Estimation of Paddy Rice Variables with a Modified Water Cloud Model and Improved Polarimetric Decomposition Using Multi-Temporal RADARSAT-2 Images

Rice growth monitoring is very important as rice is one of the staple crops of the world. Rice variables as quantitative indicators of rice growth are critical for farming management and yield estimation, and synthetic aperture radar (SAR) has great advantages for monitoring rice variables due to its all-weather observation capability. In this study, eight temporal RADARSAT-2 full-polarimetric SAR images were acquired during rice growth cycle and a modified water cloud model (MWCM) was proposed, in which the heterogeneity of the rice canopy in the horizontal direction and its phenological changes were considered when the double-bounce scattering between the rice canopy and the underlying surface was firstly considered as well. Then, three scattering components from an improved polarimetric decomposition were coupled with the MWCM, instead of the backscattering coefficients. Using a genetic algorithm, eight rice variables were estimated, such as the leaf area index (LAI), rice height (h), and the fresh and dry biomass of ears (Fe and De). The accuracy validation showed the MWCM was suitable for the estimation of rice variables during the whole growth season. The validation results showed that the MWCM could predict the temporal behaviors of the rice variables well during the growth cycle (R2 > 0.8). Compared with the original water cloud model (WCM), the relative errors of rice variables with the MWCM were much smaller, especially in the vegetation phase (approximately 15% smaller). Finally, it was discussed that the MWCM could be used, theoretically, for extensive applications since the empirical coefficients in the MWCM were determined in general cases, but more applications of the MWCM are necessary in future work.

Extension of the Monte Carlo Coherent Microwave Scattering Model to Full Stage of Rice

The ear layer, which is a component of rice, is crucial to rice monitoring and yield estimation. By adding in the ear layer, we have extended the original coherent microwave scattering model, which is based on both the first-order solution of the radiation transfer equation and Monte Carlo numerical simulation methods, to full stage of rice. The detailed scene generation and the geometrical description of rice elements are presented. The propagation path of scattering in rice canopy is reduced. Two approximation methods are used to fit the curving ear by straight cylinders. Ground truth measurements of rice fields in heading stage, including the curvature of ear, were acquired extensively at Jinhu, Jiangsu in eastern China. Measured parameters are used in the new extended model to calculate the C-band backscattering coefficients of rice field. The simulation results are used for comparison with the backscattering coefficients extracted from RADARSAT-2 images to test the validity of the coherent scattering model with the mean absolute error reaching <; 3.3 dB in the copolarization mode. Theoretical analysis reveals that the ear morphology can largely affect the backscattering behavior of the rice field.

Retrieval of Water Depth of Coastal Wetlands in the Yellow River Delta From ALOS PALSAR Backscattering Coefficients and Interferometry

Coastal wetland ecosystems are among the most productive yet highly threatened systems in the world, and population growth and increasing economic development have resulted in extremely rapid degradation and loss of coastal wetlands. Synthetic aperture radar (SAR) has proved to be of great potential in wetland applications, such as characterizing wetland types and mapping wetland inundation extent, based on the fact that the SAR backscatter signal from the wetland depends mainly on the wetland vegetation and hydrodynamic. However, the absence of ground observations has limited calibration and validation of water depth estimated from satellite SAR data. This letter aims to explore the potential for retrieving the water depth in freshwater marshes using L-band SAR backscattering coefficients. We present an innovative approach for combining backscattering coefficients and interferometric data to estimate water depth from L-band SAR data. The technique was applied to freshwater marshes in the Yellow River Delta using an Advanced Land Observing Satellite Phased Array L-band SAR data set acquired between 2007 and 2010. It is shown that the combination of L-band backscattering coefficients and interferometric data could provide information on the hydrodynamic of the Yellow River Delta with high spatial resolution.

Modeling Microwave Backscattering From Parabolic Rice Leaves

Scattering from rice leaves contributes substantially to total vegetation canopy backscattering and detailed knowledge about it is necessary for developing a microwave scattering model. A parabolic curve is generally adopted to simulate the leaf shape but this is rarely incorporated into the calculation of the scattering. In this paper, two specific models, one based on physical optics (PO) approximation and the other on the discrete dipole approximation (DDA), are presented to involve the parabolic leaf curvature effects. Three typical leaves were chosen from 1433 parabolic leaves obtained during ground measurements. The PO and DDA models were used to calculate the leaf scattering. The generalized Rayleigh–Gans (GRG) approximation was also included in the simulation. The method of moments, a computational electromagnetic method, was utilized to evaluate the accuracy of each model. Validation of the models was conducted at incidence angles ranging from 10° to 60°, incidence azimuthal angles ranging from 0° to 360°, and incidence frequencies of 1.2 GHz (L-band), 5.4 GHz (C-band), and 9.65 GHz (X-band). Among the GRG approximation, the DDA model and the PO model, the latter gave the best accuracy −>65% in the cases tested, while the GRG model was the least accurate. The high accuracy of the PO model was maintained at both the low and high frequency bands. The PO model, therefore, has great potential for use to interpret radar measurements from rice fields and other types of vegetation canopy.

A two layer water cloud model

Two layer water cloud model (WCM2), which is a refined version of the conventional water cloud model (WCM), considering the vertical inhomogeneity in the vegetation layer. The vertical inhomogeneity of the vegetation layer is described by the distribution of water content per unit volume. A piecewise linear function is used to describe the distribution of water content per unit volume. We analyze the variation tendency of vegetation scattering under different vertical inhomogeneity conditions by means of WCM2 and validate it using a physically-based model developed at Tor Vergata University, Rome, Italy. The result shows that the vertical inhomogeneity affects vegetation scattering significantly. Comparison between model predications and field measurements on radar backscattering coefficients for soybean shows WCM2 has the potential to get a better prediction result.

Inversion of Rice Biophysical Parameters Using Simulated Compact Polarimetric SAR C-Band Data

Timely and accurate estimation of rice parameters plays a significant role in rice monitoring and yield forecasting for ensuring food security. Compact-polarimetric (CP) synthetic aperture radar (SAR), a good compromise between the dual- and quad-polarized SARs, is an important part of the new generation of Earth observation systems. In this paper, the ability of CP SAR data to retrieve rice biophysical parameters was explored using a modified water cloud model. The results showed that S1 was superior to other CP variables in rice height inversion with a coefficient of determination (R2) of 0.92 and a root-mean-square error (RMSE) of 5.81 cm. RL was the most suitable for inverting the volumetric water content of the rice canopy, with an R2 of 0.95 and a RMSE of 0.31 kg/m3. The m-χ decomposition produced the highest accuracies for the ear biomass: R2 was 0.89 and RMSE was 0.17 kg/m2. The highest accuracy of leaf area index (LAI) retrieval was obtained for RH (right circular transmit and horizontal linear receive) with an R2 of 0.79 and a RMSE of 0.33. This study illustrated the capability of CP SAR data with respect to retrieval of rice biophysical parameters, especially for height, volumetric water content of the rice canopy, and ear biomass, and this mode may offer the best option for rice-monitoring applications because of swath coverage.

Ocean Wave Information Retrieval Using Simulated Compact Polarized SAR from Radarsat-2

The main objective of this paper is to demonstrate the capability of compact polarized (CP) synthetic aperture radar (SAR) to retrieve ocean wave field parameters. Souyris’ and Nord’s algorithms are used to carry out the reconstruction of CP SAR pseudo quad-polarized data for the ocean surface under both the circular transmit linear receive (CTLR) and π/4 mode. The results show that, for the CP reconstruction, Nord’s algorithm has a better convergence ability than Souyris’. In addition, the investigation of the reconstruction accuracy shows that the CTLR mode is superior to the π/4 mode, in terms of ocean surface reconstruction. It is, therefore, concluded that the reconstructed parameters of CP CTLR mode data by Nord’s algorithm adapt to retrieve ocean wave information. The ocean wave slope spectrum and other main wave parameters are also calculated from reconstructed CP data and compared with measurements from in situ National Data Buoy Center (NDBC) matched-up buoys. Comparison of CP SAR-based wave field information with buoy outputs also shows good agreement in the case of dominate wave height, wave direction, and wave period, with biases of 0.36u2009m, 17.96°, and 0.88u2009s, respectively.