基于锥角计算的Phong混合反射模型反射参数估算

针对Phong混合反射模型中漫反射和镜面反射比例难以估算问题,提出一种实用的镜面反射系数及高光指数的估算方法。首先,采用边缘检测与链码跟踪相结合的方法,精确确定可观察到镜面反射的区域的轮廓范围;再利用基于朗伯体反射模型的SFS方法,得到由于存在镜面反射而形成的亮斑的边缘点梯度值,并计算出表示可观察到镜面反射的区域的锥角大小;最后,利用合成图像找出不同镜面反射系数下高光指数与锥角间的关系式,从而实现高光指数的估算。仿真结果表明,该方法能够有效提高SFS的重构精度及普适性。

Regression Relationship between WI and FMC at Different Growth Periods of Sawtooth Oaks Leaf

[Objective] The aim was to study the regression relationship between water index （WI） and fuel moisture content （FMC） of different growth periods of sawtooth oaks leaf.[Method] Taking sawtooth oaks in Huaguo Mountain,Lianyungang City as research object,the sensitivity of WI to leaf FMC was studied at leaf level,and statistical characteristics were analyzed.[Result] The WI of sawtooth oaks leaves was sensitive to the changes of FMC,and the line regression level between them was significant.A fitting curve between leaf FMC and WI was obtained.[Conclusion] The research provides reference for acquisition methods of vegetation water remote sensing within the range of study area.

Monitoring of Growth Parameters of Sweet Corn Using CGMD302 Spectrometer

[Objective] This study aimed to clarify the quantitative relationships between growth parameters of sweet corn at different growth stages and canopy spectral indices,thus providing references for rapid monitoring of growth parameters of sweet corn.[Method] Based on field experiments of Zhengtian 68 under different potassium application levels,canopy spectral reflectance was collected using CGMD302 spectrometer at jointing stage,big bell mouth stage and tasseling stage,respectively;plant height,stem diameter,and leaf area index（LAI） were measured,to investigate the relationships between various growth parameters and spectral indices.[Result] Normalized difference vegetation index（NDVI） was positively correlated with stem diameter and LAI;ratio vegetation index（RVI） was negatively correlated with stem diameter and LAI.The single-stage monitoring models established based on NDVI and RVI could retrieve effectively stem diameter and LAI of sweet corn with the prediction accuracy of higher than 0.9,root mean square error（RMSE） of less than 10%,average relative error（RE） of less than 5%.[Conclusion] This study provided a technical basis for rapid monitoring of growth parameters of sweet corn.

Dynamic Variation and Simulation of Extinction Coefficient of Corn Population

[Objective] The aim was to study the dynamic variation of extinction coefficient of corn population, so as to improve the accuracy of assessment on net primary productivity （NPP） or yield. [Method] Based on the data of photosynthetic active radiation and leaf area index during corn growing season （from May to September） in 2006, observed in Jinzhou observation station of corn farmland ecosystem, China Meteorological Administration, the dynamic variation of extinction coefficient of corn population was analyzed. [Result] There was a great daily variation in the extinction coefficient of corn population during growing season, and the maximum value appeared from 7：00 to 9：00 and from 15：00 to 17：00, while the minimum could be found around 12：00, but the amplitude of variation decreased in tasseling stage. On a large time scale （5 d）, there was a parabolic relationship between extinction coefficient （K） and leaf area index （LAI）, with determination coefficient R2 of 0.960 7. The simulation equation of extinction coefficient, based on the sun elevation angle or leaf area index, had poor accuracy at various time during growing season, so a new dynamic model of extinction coefficient was established, namely K=λ（0.784 8-0.001 6θ）（0.154 8LAI2-0.558 6LAI＋0.654）. [Conclusion] The effect of sun elevation angle and leaf area index on extinction coefficient during corn growing season was considered in the new dynamic model of extinction coefficient, and its simulated result was superior to that of single-factor model.

A Spectral Index for Estimating Soil Salinity in the Yellow River Delta Region of China Using EO-1 Hyperion Data

Soil salinization is one of the most common land degradation processes. In this study, spectral measurements of saline soil samples collected from the Yellow River Delta region of China were conducted in laboratory and hyperspectral data were acquired from an EO-1 Hyperion sensor to quantitatively map soil salinity in the region. A soil salinity spectral index (SSI) was constructed from continuum-removed reflectance (CR-reflectance) at 2052 and 2203 nm, to analyze the spectral absorption features of the salt-affected soils. There existed a strong correlation (r = 0.91) between the SSI and soil salt content (SSC). Then, a model for estimation of SSC with SSI was established using univariate regression and validation of the model yielded a root mean square error (RMSE) of 0.986 and an R2 of 0.873. The model was applied to a Hyperion reflectance image on a pixel-by-pixel basis and the resulting quantitative salinity map was validated successfully with RMSE = 1.921 and R2 = 0.627. These suggested that the satellite hyperspectral data had the potential for predicting SSC in a large area.

Multi-Bit Sliding Stack Decoding Algorithm for OVXDM

Overlapped x domain multiplexing(OVXDM) is a promising encoding technique to obtain high spectral efficiency by utilizing inter-symbol interference(ISI) intelligently. However, the computational complexity of maximum likelihood sequence detection(MLSD) increases exponentially with the growth of spectral efficiency in OVXDM, which is unbearable for practical implementations. In this paper, based on a novel path metric associating adjacent symbols, we propose a multi-bit sliding stack decoding(Multi-Bit SSD) algorithm to achieve multiple-bit decoding simultaneously in OVXDM. Theoretical analysis is provided for the algorithm, which indicates the relationship between the performance and parameters including multiplexing waveform, overlapping fold and sliding window size. Simulation results show that the proposed algorithm can achieve better decoding performance and higher spectral efficiency than conventional fast decoding algorithms.

Satellite-based Estimates of Canopy Photosynthetic Parameters for an Alpine Meadow in Northern Tibet

Plant photosynthesis is the fundamental driver of all the biospheric functions. Alpine meadow on the Tibetan Plateau is sensitive to rapid climate change, and thus can be considered an indicator for the response of terrestrial ecosystems to climate change. However, seasonal variations in photosynthetic parameters, including the fraction of photosynthetically active radiation by canopy(FPAR), the light extinction coefficient(k) through canopy, and the leaf area index(LAI) of plant communities, are not known for alpine meadows on the Tibetan Plateau. In this study, we used field measurements of radiation components and canopy structure from 2009 to 2011 at a typical alpine meadow on the northern Tibetan Plateau to calculate these three photosynthetic parameters. We developed a satellite-based(NDVI and EVI) method derived from the Beer-Lambert law to estimate the seasonal dynamics of FPAR, k,and LAI, and we compared these estimates with the Moderate Resolution Imaging Spectroradiometer(MODIS) FPAR(FPAR_MOD) and LAI product(LAI_MOD). The results showed that the average daily FPAR was 0.33, 0.37 and 0.35, respectively, from 2009 to 2011, and that the temporal variations could be explained by all four satellite-based FPAR estimations, including FPAR_MOD, an FPAR estimation derived from the Beer-Lambert law with a constant k(FPAR_LAI), and two FPAR estimations from the nonlinear functions between the ground measurements of FPAR(FAPRg) and NDVI/EVI(FPAR_NDVI and FPAR_EVI). We found that FPAR_MOD seriously undervalued FPARg by over 40%. Tower-based FPAR_LAI also significantly underestimated FPARg by approximately 20% due to the constant k(0.5) throughout the whole growing seasons. This indicated that using FPAR_LAI to validate the FPAR_MOD was not an appropriate method in this alpine meadow because the seasonal variation of k ranged from 0.19 to 2.95 in this alpine meadow. Thus, if the seasonal variation of k was taken into consideration, both FPAR_NDVI and FPAR_EVI provided better descriptions, with negligible overestimates of less than 5% of FAPRg(RMSE=0.05), in FPARg estimations than FPAR_MOD and FPAR_LAI. Combining the satellite-based(NDVI and EVI) estimations of seasonal FPAR and k, LAI_NDVI and LAI_EVI derived from the Beer-Lambert law also provided better LAIg estimations than LAI_MOD(less than 30% of LAIg). Therefore, this study concluded that satellite-based models derived from the Beer-Lambert law were a simple and efficient method for estimating the seasonal dynamics of FPAR, k and LAI in this alpine meadow.