地面站点叶面积指数观测的空间代表性评价——以CERN站网观测为例

在叶面积指数LAI(Leaf Area Index)产品真实性检验中,地面站点的多时相连续观测LAI数据是重要的验证数据来源。当站点观测范围与产品像元尺度不一致时,站点观测LAI直接用于产品验证可能为验证结果带来误差。因此,在验证之前需要分析站点观测对像元尺度的空间代表性,选择空间代表性好的观测来验证产品,从而减小尺度效应带来的验证误差。以往的研究只是简单的定性说明研究区域,并直接用站点测量数据对产品进行验证,缺少一套系统的站点观测在产品像元尺度内空间代表性评价的方法体系。本文提出了站点LAI观测的空间代表性评价方法,建立了评价指标DVTP(Dominant Vegetation Type Percent)、RSSE(Relative Spatial Sampling Error)和CS(Coefficient of Sill),构建了空间代表性评价分级体系。以中国生态系统研究网络CERN(Chinese Ecosystem Research Network)农田站和森林站LAI观测为例,对站点观测在1 km产品像元尺度内空间代表性进行评价,并分析评价前后站点观测对MODIS LAI产品验证精度的影响。结果显示,本文提出的方法能够有效地对不同站点LAI观测在产品像元尺度内空间代表性进行质量分级,且年际间的站点观测空间代表性较为一致。评价方法能够去掉在特定产品像元尺度下空间代表性不好的观测数据,一定程度上提高验证数据集对产品验证精度的可靠性。     

Estimation of evapotranspiration and its components from an apple orchard in northwest China using sap flow and water balance methods

Field experiments were conducted to investigate the effects of leaf area index and soil moisture content on evapotranspiration and its components within an apple orchard in northwest China for 2 years. Evapotranspiration in the non‐rainfall period was estimated using two approaches: the soil water balance method based on tube‐type time‐domain reflection measurements, and sap flow plus micro‐lysimeter methods. The two methods were in good agreement, with differences usually less than 10%. The components of evapotranspiration varied with canopy development. During spring and autumn, soil evaporation was dominating as result of low leaf area index. In summer, plant transpiration became significant, with an average transpiration to evapotranspiration ratio of 0·87. The crop coefficient K_c showed a strong linear dependence on leaf area index. The water stress coefficient K_s was around 1·0 when soil moisture was above 23% and started to decrease linearly after that. This study demonstrates that prediction of evapotranspiration in apple orchards can be made using the Food and Agriculture Organization's crop coefficient method from commonly available meteorological data in the area. Copyright © 2006 John Wiley & Sons, Ltd.     

A fusion-based neural network methodology for monthly reservoir inflow prediction using MODIS products

ABSTRACT

A model fusion approach was developed based on five artificial neural networks (ANNs) and MODIS products. Static and dynamic ANNs – the multi-layer perceptron (MLP) with one and two hidden layers, general regression neural network (GRNN), radial basis function (RBF) and nonlinear autoregressive network with exogenous inputs (NARX) – were used to predict the monthly reservoir inflow in Mollasadra Dam, Fars Province, Iran. Leaf area index and snow cover from MODIS, and rainfall and runoff data were used to identify eight different combinations to train the models. Statistical error indices and the Borda count method were used to verify and rank the identified combinations. The best results for individual ANNs were combined with MODIS products in a fusion model. The results show that using MODIS products increased the accuracy of predictions, with the MLP with two hidden layers giving the best performance. Also, the fusion model was found to be superior to the best individual ANNs.     

用地面点测量数据验证LAI产品中的尺度转换方法

遥感叶面积指数产品精度和不确定性需要通过地面测量的数据来验证。因为两者的空间尺度差异,验证前需要通过尺度转换方法将这两种数据的尺度统一。将泰勒级数展开模型进行改进后,可以用于解决叶面积指数遥感产品验证中地面测量数据与遥感产品尺度不匹配的问题,同时可以针对每个数据定量给出误差。将误差在阈值范围内的实测数据作为地面参考值对遥感产品进行验证。本文利用这种方法将黑河综合遥感联合实验数据集中的地面测量叶面积指数尺度上升到遥感像元尺度,并与MODIS,GLASS LAI遥感产品进行了比较。     

Simulation analysis on the relationship between the leaf area index and polarimetric parameters of crops

Abstract

The leaf area index (LAI) is an important parameter to quantitatively describe the structure of vegetation and crops. Uncertainty in the relationship between the LAI and polarimetric parameters is the key problem for LAI estimation from polarimetric synthetic aperture radar (POLSAR) data. However, the existing POLSAR data have difficulties meeting the demand of the aforementioned research. This paper analyses the correlations between the LAI and the polarimetric parameters derived from Cloude and Freeman decompositions using simulated POLSAR data based on a coherent scattering model for maize and wheat. The results show: (1) The POLSAR data at C-band with a large incidence angle (40 degrees) are very suitable for finding the LAI for maize and wheat. (2) For maize there is a strong correlation between the scattering type angle and the LAI at C-band with a large incidence angle, and the coherency entropy, anisotropy, and the power of the double-bounce scattering power component also have significant correlations with the LAI. (3) For wheat at C-band with a high incidence angle, although the correlation coefficient is low, there is still a correlation between the entropy, anisotropy and LAI. Besides, the volume scattering is suitable for extracting the LAI for wheat at X-band.     

机载激光雷达森林叶面积指数反演研究

叶面积指数(LAI)是分析冠层结构最常用的参数之一,它控制着植被的生物物理过程,如光合、呼吸、蒸腾、碳循环和降水截获等,因此快速、可靠和客观地评价LAI非常重要.本文发展了激光穿透指数(LPI)的简化计算方法,首次利用校正后的回波强度计算出LPI,以LPI为变量基于Beer-Lambert定律实现了甘肃大野口研究区森林LAI反演,并且与原始回波强度和回波数反演LAI的精度进行对比,结果发现通过距离和角度校正后的回波强度值能提高LAI反演精度.为了评价模型的可靠性和泛化性能,用留一法交叉验证程序(LOOCV)对最佳反演模型进行了验证,表明该模型没有过度拟合,具有很好的泛化能力.最后,用没有参加建模的16个实测LAI对预测值进行精度验证(R²=0.810,RMSE=0.198),发现校正后的回波强度反演山区森林LAI精度较高.本文还对激光雷达LAI反演结果与传统光学TM影像的反演结果进行了对比分析,结果表明机载激光雷达反演LAI精度(R²=0.825,RMSE=0.165)高于光学TM遥感数据(R²=0.605,RMSE=0.257),因此,可用激光雷达数据实现研究区的高精度LAI反演,为生态环境研究提供可靠的基础数据.     

A phytolith index as a proxy of tree cover density in tropical areas: calibration with Leaf Area Index along a forest–savanna transect in southeastern Cameroon

The aim of the study is to calibrate the phytolith index of tree cover density, D/P (the ratio of ligneous dicotyledons phytoliths (D) over Poaceae phytoliths (P)) with Leaf Area Index (LAI) measurements. LAI is the vertically integrated surface of leaves per unit of ground area (m² leaves/m² ground). Modern soil samples from southeastern Cameroon, collected along a continuous forest–savanna transect, have been analyzed for phytoliths. Phytolith assemblages and D/P index clearly record the physiognomy of the forest and savanna communities and of the transition between both of them. A highly significant relationship was obtained between D/P and LAI. The relationship between phytolith data and the vegetation transect is also discussed and compared with existing palynological results obtained along the same transect.     

Crop water relationships and thermal adaptation of kathika beans (Phaseolus vulgaris L.) and green grams (Vigna radiata L. Wilczek) with special reference to temporal patterns of potential growth in the drylands of SE-Kenya

High soil temperatures influence seedling emergence as well as the performance of plant organs and yields. Temperatures of above 40°C were recorded in the topsoil of the Ferralsols of the semi-arid areas of SE-Kenya during the rainy season. Experiments were conducted in the climatological laboratory of the University of Trier, Germany, on two legumes (green grams and common beans var. kathika), grown by smallholder farmers in the study area under varying soil temperature conditions. The main objective was to examine the effect of soil temperature on various parameters: seedling emergence, crop water requirements, leaf area index and phenology of the two legumes. The simulated climatological laboratory conditions were similar to those at Kiboko, SE-Kenya. This study confirmed that under high soil temperature conditions, green grams are well adapted to semi-arid and hot tropical lowlands as well as lower midlands due to low water requirements, high seedling emergence rates and good yield performance. However, kathika beans were very susceptible to the simulated extreme climatic environment. By means of crop simulation modelling, a temporal differentiation for potential growth of green grams is presented for the long and short rains according to different rainfall conditions (ENSO, antiENSO, normal) over a period of 31 years.     

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。