Wangfei Zhang

Compact Polarimetric Response of Rape (Brassica napus L.) at C-Band: Analysis and Growth Parameters Inversion

Growth parameters like biomass, leaf area index (LAI) and stem height play an import role for crop monitoring and yield prediction. Compact polarimetric (CP) SAR has shown great potential and similar performance to fully-polarimetric (FP) SAR in crop mapping and phenology retrieval, but its potential in growth parameters inversion has not been fully explored. In this paper, a time series of images of CP SAR was simulated from five FP SAR data gathered during the entire growth season of rape. CP response of 27 parameters, relying on Stokes parameters and their child parameters, decomposition parameters and backscattering coefficients, were extracted and investigated as a function of days after sowing (DAS) during the whole rape growth cycle to interpret their sensitivity to each growth parameter. Then, random forest (RF) was chosen as an automatic approach for the growth parameters inversion method, and its results were compared with traditional single-parameter regression models. Most of the CP parameters showed high sensitivity with growth parameters and great potential for growth parameters inversion. Among all of the regression models, the quadratic regression model showed the best performance for all of the growth parameters inversion, the best result for biomass inversion was the third component of the Stokes parameters (g3) with R2 of 0.765 and RMSE of 73.20 g/m2. For LAI and stem height was one of the Stokes child parameters, the circular polarization ratio (Uc), with R2 of 0.857 and 0.923 and RMSE of 0.66 and 18.71 cm, respectively. RF showed the highest accuracy and smallest RMSE for all of three growth parameters inversion; R2 for biomass, LAI and stem height were 0.93, 0.96 and 0.95, respectively; RMSE were 46.24 g/m2, 0.25 and 13.5 cm, respectively. However, there are also some CP parameters, which showed low sensitivity to growth parameters, that had high importance for RF inversion. The results confirmed the potential of CP data and the RF method in growth parameters inversion, but they also confirmed that it was difficult to give a physical interpretation for the RF inversion model.

Rape (Brassica napus L.) Growth Monitoring and Mapping Based on Radarsat-2 Time-Series Data

In this study, 27 polarimetric parameters were extracted from Radarsat-2 polarimetric synthetic aperture radar (SAR) at each growth stage of the rape crop. The sensitivity to growth parameters such as stem height, leaf area index (LAI), and biomass were investigated as a function of days after sowing. Based on the sensitivity analysis, five empirical regression models were compared to determine the best model for stem height, LAI, and biomass inversion. Of these five models, quadratic models had higher R2 values than other models in most cases of growth parameter inversions, but when these results were related to physical scattering mechanisms, the inversion results produced overestimation in the performance of some parameters. By contrast, linear and logarithmic models, which had lower R2 values than the quadratic models, had stable performance for growth parameter inversions, particularly in terms of their performance at each growth stage. The best biomass inversion performance was acquired by the volume component of a quadratic model, with an R2 value of 0.854 and root mean square error (RMSE) of 109.93 g m−2. The best LAI inversion was also acquired by a quadratic model, but used the radar vegetation index (Cloude), with an R2 value of 0.8706 and RMSE of 0.56 m2 m−2. Stem height was acquired by scattering angle alpha ( α ) using a logarithmic model, with an R2 of 0.926 value and RMSE of 11.09 cm. The performances of these models were also analysed for biomass estimation at the second growth stage (P2), third growth stage (P3), and fourth growth stage (P4). The results showed that the models built at the P3 stage had better substitutability with the models built during all of the growth stages. From the mapping results, we conclude that a model built at the P3 stage can be used for rape biomass inversion, with 90% of estimation errors being less than 100 g m−2.

Three-Step Semi-Empirical Radiometric Terrain Correction Approach for PolSAR Data Applied to Forested Areas

In recent decades, most methods proposed for radiometric slope correction involved the backscattering intensity values in synthetic aperture radar (SAR) data. However, these methods are not fully applicable to quad-polarimetric SAR (PolSAR) matrix data. In this paper, we propose a three-step semi-empirical radiometric terrain correction approach for PolSAR forest area data. The three steps of terrain effects correction are: polarisation orientation angle (POA), effective scattering area (ESA), and angular variation effect (AVE) corrections. We propose a novel method to determine adaptively the “n” value in the third step by minimising the correlation coefficient between corrected backscattering coefficients and the local incidence angle; we then constructed the correction coefficients matrix and used it to correct PolSAR matrix data. PALSAR-2 HBQ (L-band, quad-polarisation) data were used to verify the proposed method. After three-step correction, differences between front and back slopes were significantly reduced. Our results indicate that POA, ESA, and AVE corrections are indispensable steps to producing PolSAR data. In the POA correction step, horizontal–vertical (HV) polarisation was maximally influenced by the POA shift. The max deviation of the POA correction was greater than 1 dB for HV polarisation and approximately 0.5 dB for HH/VV polarisation at an intermediate shift angle (±20°). Based on Light Detection and Ranging (LiDAR)-derived forest aboveground biomass (AGB) data, we analysed the relationship between forest AGB and backscattering coefficient; the correlation was improved following the terrain correction. HV polarisation had the best correlation with forest AGB (R = 0.81) and the correlation improved by approximately 0.3 compared to the uncorrected data.

Retrieval of forest above ground biomass using automatic KNN model

Forest is an important component of terrestrial ecosystems, so it is necessary to estimate the forest aboveground biomass (AGB) accurately in order to reduce the uncertainty of the carbon stock in forest ecosystem. In this study, a fast, efficient and automatic method has been proposed, called as Automatic KNN(AKNN). Using the Landsat 8 OLI data, airborne polmetric SAR (PolSAR) data, the AKNN has been applied to estimate the forest AGB and stem volume at two levels, the pixel and the subcompartment levels, respectively. The results showed that the power of AKNN in quantitative retrieval at two levels(R2=0.75, RMSE=23.43t/ha; R2=0.52 and RMSE=21.86m3/ha, respectively)

Land degradation assessment by applying relative rue in Inner Mongolia, China, 2001–2010

Land degradation in Inner Mongolia, China is much severe. Remote sensing application on land degradation assessment can provide scientific basis for land degradation prevention in the study area. In this paper, land degradation was assessed by applying two improved relative Rain Use Efficiency (RUE) indicators based on time series MODIS NDVI data and high-resolution meteorological data from 2001 to 2010. The results show that 76.74% land of the whole study area with good or unusually good condition, it indicates that the most areas have normal or good vegetation production capacity. The unusually degraded and degraded lands account for 11.94% of the study area, especially they are less degraded lands distributing in Beijing and Tianjin sandstorm source region within the Inner Mongolia, it indicates that some ecological engineering projects implemented in this area have achieved significantly for restoration of degraded ecosystems in recent 10 years.

Terrain effect correction method for Insar ILU image

In this paper, we focus on the topographic effects of InSAR interferometric land use (ILU) images and a corresponding terrain correction method is proposed. Firstly, Effective scattering area correction and angular variation effect correction are performed for intensity information. Secondly, for the coherence information, a novel difference equation method of terrain correction based on the SINC volume decorrelation model is proposed. The Tandem-X/Terrasar-X InSAR data and SRTM DEM data are used to illustrate the method. The results showed that terrain effects not only existed in the intensity image, but also existed obviously in the coherence image and can been effectively removed by the proposed method. Finally, the interpretability of the ILU imagery treated in this manner is improved in comparison to the uncorrected case.

The classification results interpretation for compact SAR data based on partial polarization decomposition

With the advantages of the simpler transmitter architecture requirements, the wider swath capability and lower data rate, compact polarimetric (CP) synthetic aperture radar (SAR) was proposed in recent ten years to substitute or compensate the disadvantage of the full polarization mode SAR, especially on widening the swath width. CP mode is transmitted with one polarization and received with both of them. As there are only two channels, the decomposition methods and theories which were used for full polarization can not directly apply into it. According to the characteristics of CP mode, the decomposition theory based on partial polarized waves were developed, like the degree of polarization and phase difference (m - δ) decomposition, the degree of polarization and scattering angle (m - α), the degree polarization and the Poincare ellipticity parameter (m - χ) decomposition. Since α and χ are mutual complementary angle, the result of these two decomposition is same. Since the main purpose of decomposition is to classify the different objects, this paper focus on the classification result difference of m - δ and m - α, the interpretation of these results and how to improve the classification based on the better decomposition results. In this paper, the classification results of these two decomposition methods were compared with full polarization classification result. The classification overall accuracy of m - α is 97.17%, whereas m - δ is 88.28%. The kappa coefficient for the former is 0.8500, the latter is 0.8207. Since the volume scattering component is same, the differences were caused by surface scattering component and dihedral component. The statistics of these two components shown that α has better accumulativeness than δ, which lead to the higher classification accuracy. Both of these two method result in higher assessment of volume scattering.

Temporal decorrelation on airborne repeat pass P-, L-band T-SAR in boreal forest

The goal of this paper is to investigate the influence of temporal decorrelation on InSAR / Pol-INSAR and T-SAR in boreal forest. The P-, L-band Pol-InSAR data collected in campaign BioSAR 2008 was used in our study. The impact of temporal decorrelation on InSAR / Pol-InSAR and T-SAR is reflected by coherences, phases and vertical backscattering power. Markov model is applied to describe the quantitative impact of time decorrelation, and correlation, time decorrelation constant is identified by GA. And the influence of temporal decorrelation on T-SAR is also related with coherences and phases. The backscattering power represents more ambiguous with longer time interval than that with shorter time interval for single baseline.

Simulation of carbon flux of forest ecosystem by Biome-BGC and MODIS-PSN models

An approach was used to incorporate the forest carbon flux for Qilian Mountains by ecological-process-based model (Biome-BGC), and remote-sensing-based model (MODIS-PSN). The calibration phase, aiming at setting the ecophysiological parameters to effectively simulate the daily GPP behavior of the Qilian Mountains, was proceeded by adjusting the 8 day GPP outputs obtained from Biome-BGC using the optimized MODIS-PSN algorithm and the observations. The results showed that the optimized MODIS-PSN could describe the GPP behavior faithfully comparing to the eddy covariance-observed GPPs, with R2=0.77, RMSE=6.219gC/m2/8d. After validation, the calibrated Biome-BGC has been proved to estimate the performances of daily GPP behavior effectively comparing to the eddy covariance-observed GPPs, especially in summer and winter (with R2= 0.76, RMSE= 1.3115gC/m2/d), which illustrated that the combination of Biome-BGC and optimized MODIS-PSN could express the carbon fluxes well over the Qilian Mountains.