玉米不同生长期微波辐射特性研究

针对当前微波传感器监测植被生长信息的实验方案不完善的问题,该文重新设计实验方案,主要研究玉米长高过程中微波辐射特性及叶面积指数的微波反演。研究结果表明:随着观测角度和页面指数(LAI)的增大,V和H极化的亮温都有明显变化;微波植被指数随观测角度和LAI的变化都有明显规律;测量方位对玉米的微波辐射特性有明显的影响作用;频率6.6GHz和观测角度为60°时,可利用归一化极化差植被指数(MPDI)进行较好的LAI反演。     

Effects of water and heat on growth of winter wheat in the North China Plain

The North China Plain (NCP) was selected as the study area and the effects of water and heat were analysed to determine the dominant factor affecting winter wheat growth. The mean, minimum and maximum temperatures, precipitation and soil moisture data were selected to analyse the correlations between the leaf area index (the growth indicator) and these factors using long time series half-monthly data (2–5 months) (from 1982 to 2010). The results showed that temperature was the main factor affecting the growth of winter wheat in the NCP. The growth of winter wheat had weak correlations with precipitation and soil moisture and the influence of water on winter wheat growth was smaller than the influence of heat. In the northern part of the NCP, mainly including the north-west region of Shandong Province and the southern region of Hebei Province, irrigation was necessary in late February and early March.     

土地覆被变化过程中叶面积指数与降水量对地表能量平衡的贡献——基于SiB2的模拟结果

土地覆被变化过程中,叶面积指数（LAI）是地表能量平衡重要的影响因素,且与降水等多种因素存在交互作用。以2003 年中国东北地区农田、森林和草地三种覆被类型和三种LAI月变化过程为研究对象,着重分离出土地覆被变化过程中LAI对地表能量平衡的作用,认清覆被和LAI 变化对地表能量平衡的相对作用。利用SiB2 模型研究不同降水条件下土地覆被和LAI 变化对地表能量平衡的影响。结果表明：① 覆被变化对净辐射的影响最大,年均14.5W·m^-2左右。② LAI 主要改变净辐射对潜热和显热的分配,对农田和草地而言,LAI 增加明显提高（减小）潜热（显热）分配比例;对森林而言,LAI对潜热和显热分配的影响较弱。③ 降水对净辐射的分配起重要作用,降水增加,潜热增加。④ 表层土壤水分受降水和LAI 调控,与潜热有相反的变化趋势。     

The spatial scaling effect of continuous canopy Leaves Area Index retrieved by remote sensing

Leave Area Index (LAI) is one of the most basic parameters to describe the geometric structure of plant canopies. It is also important input data for climatic model and interaction model between Earth surface and atmosphere, and some other things. The spatial scaling of retrieved LAI has been widely studied in recent years. Based on the new canopy reflectance model, the mechanism of the scaling effect of continuous canopy Leaf Area Index is studied, and the scaling transform formula among different scales is found. Both the numerical simulation and the field validation show that the scale transform formula is reliable. Supported by National Basic Research Program of China (Grant No. 2007CB714402) and National Natural Science Foundation of China (Grant Nos. 40401036, 40734025 and 40401036)     

Estimation of gross primary production in wheat from in situ measurements

Gross primary production (GPP) is a parameter of significant importance for carbon cycle and climate change research. Remote sensing combined with other climate and meteorological data offers a convenient tool for large scale GPP estimation. This paper presents a study of GPP estimation using three methods with in situ measurements of canopy reflectance, LAI, and the photosynthetically active radiation (PAR). First, because LAI is considered as an indicator of the factor of absorbed PAR (f_APAR), it provides reasonable estimates of GPP for all types of wheat with coefficient of determination R² of 0.7353. The second method uses four kinds of vegetation indices (VIs) to estimate GPP because these indices are suggested to be reliable candidates in the estimation of light use efficiency (LUE). Good determination coefficients were acquired in estimating GPP with R² ranging from the lowest of 0.7604 for NDVI to the highest of 0.8505 for EVI. A new method was proposed for the estimation of GPP following the Monteith logic, which considering GPP as a product of VI × VI × PAR. Results indicated that this method can provide the best estimates of GPP as determination coefficient R² increased largely compared to the other two methods. EVI × EVI × PAR was demonstrated to be the most suitable for the estimation of GPP with the highest R² of 0.9207, which was about 10% larger as compared to GPP estimated from the single EVI. These results will be helpful for the development of new models of GPP estimation with all remote sensing inputs.     

基于多次散射的植被-土壤二向反射模型

运算过于复杂是目前多数植被二向反射模型存在的一个突出问题。对冠层的各次散射、透射和地面土壤反射分别进行分析和考虑 ,建立各次反射光谱分量与叶面积指数的函数关系。在Verstraete (1 986 )的物理模型的基础上加上多次散射和土壤反射的分量 ,建立了基于多次散射的植被 土壤二向反射模型 ,并证明仅考虑前三次散射即可满足一般精度要求。该模型克服了原模型的不足 ,并可根据叶面积指数等参量直接计算二向反射率 ,避免了运算量巨大的叠代运算 ,故便于应用。实验证明 ,该模型具有较高的精度     

MODIS LAI产品真实性检验与偏差分析

当前对MODIS LAI产品的真实性检验工作中,更多的是关注遥感产品在数值与趋势上与地表真值的一致性程度,很少工作能够全面分析遥感LAI产品偏差来源以及不同来源的偏差对全局偏差的贡献率。本文在对MODIS LAI产品进行真实性检验基础之上,进一步分析了MODIS LAI产品偏差来源。将遥感产品真实性检验偏差来源分解为反演模型,反射率数据和冠层聚集效应3个方面,并定量分析各个偏差源对真实性检验结果的影响。以河北省怀来玉米为研究对象,结合实测LAI数据和Landsat 8 OLI(Operational Land Imager)数据建立NDVI LAI半经验模型,得到LAI参考数据,据此对MODIS LAI产品进行真实性检验及偏差分析。研究表明,该区域MODIS LAI产品存在明显的低估现象,参考数据和MODIS LAI数据均值分别为3.53 m2/m2和2.33 m2/m2,MODIS产品低估为34.14%。在各个偏差因素中,反射率数据的差异对结果影响最大,即MODIS地表反射率数据与Landsat 8 OLI地表反射率数据的差异造成的偏差占总偏差的57.50%;聚集效应的影响次之,占总偏差的28.33%;模型差异对结果的影响最小,占总偏差的14.17%。本研究对遥感产品真实性检验及其不确定性分析具有一定的借鉴意义。     

Estimating fractional vegetation cover from leaf area index and clumping index based on the gap probability theory

Gap probability theory provides a theoretical equation to calculate fractional vegetation cover (FVC). However, the main algorithms used in present FVC products generation are still the linear mixture model and machine learning methods. The reason to limit the gap probability theory applied in the product algorithm is the availability and accuracy of leaf area index (LAI) and clumping index (CI) products. With the improvement of the LAI and CI products, it is necessary to assess whether the algorithm based on gap probability theory using the present products can improve the accuracy of FVC products. In this study, we generated the FVC estimates based on the gap probability theory (FVCgap) with a resolution of 500 m every 8 days for Europe. FVCgap estimates were validated with field FVC measurements of ImagineS from 2013 to 2015 for crop types. Two existing FVC products, Geoland2 Version1 (GEOV1) and Multisource data Synergized Quantitative remote sensing production system (MuSyQ), were used to inter-compare with the FVCgap estimates. FVCgap estimates showed a better agreement with field FVC measurements, with lowest root mean square error (RMSE) (0.1211) and bias (0.0224), than GEOV1 and MuSyQ FVC products. The inter-annual and seasonal variations of FVCgap estimates were also showed the most consistent with field measurements.     

人工蜂群算法优化SVR的叶面积指数反演

支持向量机回归SVR (Support Vector Regression)方法作为叶面积指数反演的一种新思路,在LAI反演中具有一定的应用价值和前景,但SVR算法中惩罚系数C、核函数宽度参数g、不敏感损失函数参数ε的取值对回归精度有显著的影响。本文提出了一种基于人工蜂群算法ABC (Artificial Bee Colony)优化SVR参数的遥感影像叶面积指数反演方法。研究数据为美国土壤水分实验（SMEX02） 2002年LAI实测数据和同期的Landsat 7ETM+地表反射率数据,为了验证ABC算法优化SVR各个参数对反演精度的影响,建立了未优化参数（SVR）、优化单个参数（ABC-SVR-C,ABC-SVR-g,ABC-SVR-ε）、优化3个参数（ABC-SVR）的3类LAI反演模型,并比较了其回归拟合精度。在此基础上,分析了3个关键参数对LAI反演模型精度的敏感性,并对ABC算法优化SVR模型的精度进行显著性检验。研究表明：（1）相比未优化参数模型,ABC算法优化模型具有更高的反演精度,优化3个参数优于优化单个参数,回归直线斜率k达到0.797、决定系数r2达到0.775。（...     

基于背景库的高质量LAI时间序列数据重建

叶面积指数LAI(Leaf Area Index)是表征植被冠层结构的重要参数,然而由于云等大气因素的影响,MODISLAI时间序列产品在时间与空间尺度的连续性仍存在问题。随着先验知识在遥感反演中的地位不断得到加强,本文将多年LAI历史数据作为先验知识,用以建立LAI背景库,并提出了基于LAI背景库的Savitzky-Golay(SG)滤波算法来实现LAI时间序列数据的降噪工作。结果表明,与传统SG滤波相比,新算法能够更加客观有效地重建LAI时间序列。