东北主要绿化树种叶面积指数(LAI)高光谱估算模型研究

以东北主要绿化树种为研究对象,分别在长春市南湖公园和长春公园获取了共240组树冠高光谱反射率及相应的LAI数据。对数据进行相关分析,以确定反演LAI的敏感波段,而后分别运用6种植被指数、神经网络以及小波分析等3种方法进行估算。研究结果表明,3种方法估算树冠LAI都取得了较好的效果:①与RVI、NDVI相比,由DVI、RDVI、MSAVI、TVI等植被指数建立的估算模型可以提高LAI的估算精度;②神经网络在拟合光谱反射率与树冠LAI关系时明显优于植被指数法(R2达0.850);③小波能量系数与LAI相关性较好,单变量回归分析R2可达0.683,部分小波能量系数估算LAI的精度优于植被指数法,并且验证R2也较高,说明其稳定性较好,多元变量回归分析能够实现各小波能量系数间的优势互补,R2可达0.794。     

玉米叶面积指数与高光谱植被指数关系研究

探讨以不同的植被指数建立的高光谱模型对玉米叶面积指数LAI的反演精度。实测不同水肥耦合作用下，玉米冠层的高光谱反射率与叶面积指数（Leaf Area Index）数据，采用高光谱红光波段（631~760 nm）与近红外波段（760~1 074 nm）逐波段构建NDVI、RVI、DVI、TSAVI、PVI植被指数，分别找出与LAI具有最佳相关性波段组合的植被指数，建立玉米LAI估算模型。结果显示，与LAI具有佳相关性的波段组合分别是NDVI（R760，R990）、RVI（R760，R1001）、DVI（R677，R1070）、TSAVI（R 760，R 975）、PVI（R658，R966），它们反演玉米LAI的确定性系数分别：R²>0.72、R²>0.74、R²＝0.95、R²>0.79、R²>0.95。结果表明，在玉米的整个生长季的47个样本中，通过PVI和DVI方式建立的遥感估算模型能够较为准确地估算玉米LAI，TSAVI次之，NDVI、RVI稍差。     

大豆叶面积的高光谱模型

以ASD FieldSpec-Vnir光谱仪实测不同生长季大豆的冠层反射率,同期采集对应大豆LAI,然后逐波段分析冠层光谱反射率、导数光谱与大豆LAI的相关关系;并采用单变量线性回归逐波段分析了冠层光谱反射率、导数光谱与大豆LAI确定性系数随波长的变化趋势,建立了以近红外与可见光波段冠层光谱反射率的比值植被指数RVI与大豆LAI的高光谱遥感估算模型。结果表明,冠层光谱反射率在350￣680nm、760￣1050nm波谱区与大豆LAI相关性较大,而在红边区680￣760nm的相关性变化较大;导数光谱在红边区与大豆LAI相关程度高。通RVI方式建立的遥感估算模型能较为准确估算大豆LAI,通过对红外与蓝波段建立的RVI指数与大豆LAI的回归模型,表明其预测大豆LAI的能力较好,有进一步研究的必要;通过对比发现,神经网络模型可以大大提升高光谱反演大豆LAI的水平,模型的确定系数R2为0.9661,而总均方根误差RMSE仅为0.446m2.m-2。     

基于宽波段和窄波段植被指数的草地LAI反演对比研究

叶面积指数是一个重要的植被生理生态参数,为探讨不同植被指数反演叶面积指数的可行性,基于同空间分辨率不同光谱分辨率的HJ\|1B CCD1和Hyperion遥感影像数据,以内蒙古自治区赤峰市克斯克腾旗贡格尔草原为研究对象,选取几种常见宽波段植被指数和高光谱窄波段植被指数并结合4种常用回归模型,比较分析了不同植被指数反演叶面积指数的精度。结果表明:对于全部植被指数而言,PVI、MSAVI等综合考虑了土壤、环境等因素的植被指数较传统植被指数NDVI、RVI反演草地LAI精度更高。通过对比发现,在反演草地LAI方面,窄波段植被指数比宽波段植被指数表现出明显的优势。其中,窄波段垂直植被指数PVI验证模型的确定性系数R²为0.65,均方根误差RMSE为0.15,说明实测LAI和模拟LAI值之间具有较好的变化一致性。最后基于Hyperion影像和窄波段垂直植被指数PVI的估算模型生成研究区叶面积指数空间分布图。     

基于高光谱植被指数的加工番茄生长状况监测研究

利用ASD地物非成像高光谱仪,获取2个加工番茄品种4水平施氮量和3种配置种植方式6个关键生育时期冠层的反射光谱数据,通过计算得到归一化植被指数(NDVI)、比值植被指数(RVI)、修改型二次土壤调节植被指数(MSAVI2)和红边归一化植被指数(RENDVI),并分别与其冠层叶绿素密度(CH.D)、叶面积指数(LAI)、地上鲜生物量(AFBM)和地上干生物量(ADBM)进行相关分析,经检验,相关系数均达到1%的极显著水平。其中RENDVI与CH.D的线性相关模型,RVI与LAI的幂指数函数模型的相关性最好(RRENDVI-CH.D=0.8034**,RRVI-LAI=0.8703**,n=54,α=1%),用上述2个相关模型方程分别估算加工番茄CH.D和LAI,实测值与估测值之间均呈极显著的线性相关关系(R实测CH.D-估测CH.D=0.8113**,R实测LAI-估测LAI=0.8546**,n=54,α=1%),估算精度分别为85.5%和86.3%。试验结果表明,用高光谱植被指数,可以对加工番茄冠层CH.D、LAI、AFBM和ADBM进行遥感估算,实现对加工番茄生长状况的实时、无损、非接触和定量的高光谱监测研究。     

基于光谱分析的长江口湿地互花米草叶片叶绿素含量反演研究

互花米草是我国滨海湿地主要入侵植物之一,对当地生态系统产生深远影响,其叶绿素含量信息是湿地生态系统关键生态功能定量化研究的重要基础数据。以长江口崇明东滩湿地为研究区,以实测互花米草叶片光谱反射率和总叶绿素含量为数据源,在400~1 000nm范围内研究原始光谱反射率和一阶导数光谱反射率的比值形式(RVI)与归一化差值形式(NDVI)组合,与叶绿素含量的相关性分析,并构建叶绿素含量估算模型。结果表明:基于原始光谱反射率的RVI和NDVI植被指数形式所构建的模型的精度最高,均方根误差RMSE分别达到0.24和0.25;一阶导数光谱反射率因噪声影响较大,其估算效果不佳;从模型所入选波段来看,红边波段在互花米草叶绿素含量估算中尤为重要。     

基于MODIS数据的玉米植被参数估算方法的对比分析

基于实测数据建立了FPAR、LAI的植被指数估算模型（NDVI、RVI、NDWI）,并将其应用于MODIS BRDF数据对德惠地区玉米FPAR、LAI进行估算,然后将MODIS 15A2 FPAR/LAI产品值分别与BRDF估算值、地面实测值进行对比分析。主要得出以下结论：植被指数NDVI、RVI都能较好地用于实测数据和MODIS BRDF数据的FPAR、LAI估算;NDWI虽然在实测数据中估算玉米FPAR、LAI的效果优于NDVI、RVI,但其应用于MODIS BRDF数据估算FPAR、LAI时,效果却较差。BRDF数据估算FPAR与MODIS 15A2 FPAR值的关系因生长时期不同而异,在玉米生长前期,前者高于后者,而生长后期两者却较相近;BRDF估算LAI值一直都高于MODIS 15A2 LAI产品值。生长季前期,MOD15A2 FPAR、LAI值接近实测值,而在后期却高于实测值。通过分析也表明,玉米苗期MODIS 15A2 FPAR数值变化范围较小,产品算法对实际FPAR变化尚不够敏感,这可能是影响MODIS FPAR产品精度的一个原因。     

基于SPOT5遥感影像丰宁县植被地上生物量估测研究

利用SPOT5遥感影像数据和同期获得的野外调查样地数据,基于按植被类型分类估测的方法,研究了河北省丰宁满族自治县植被地上生物量的遥感估测技术。研究结果显示,SPOT5影像的4个波段反射率和中红外植被指数(VI3)结合建立的多元回归模型,可用于森林生物量的遥感估测,估测的R2值达0.540,说明中红外波段信息提高森林生物量的估测精度有一定作用;通过分析样地生物量与多种植被指数的相关性发现,基于比值植被指数(RVI)的指数回归模型是灌丛生物量估测的最佳模型,估测的R2值达0.711,基于归一化植被指数(NDVI)的简单线性回归模型为估测草地生物量的最佳模型,R2值达0.790。利用2008年的全覆盖SPOT5影像,获得了丰宁县2008年植被地上生物量分布图,除农田植被外,全县地上生物总量为3.706×107 t,单位面积生物量平均为51.223t/hm2,其中,森林植被总生物量为3.578×107 t,灌丛植被总生物量为1.048×106 t,草地植被总生物量为2.277×105 t。     

水稻冠层光谱变化特征的土壤重金属全量反演研究

通过水稻冠层光谱变化特征可直接或者间接反映耕地重金属污染状况。研究在光谱分析的基础上,采用植被指数(包括NDVI、DVI、SR、VOG、mSR705、SIPI)和相关分析相结合的方法提取与土壤中Cd、Pb、Zn相关的水稻冠层光谱敏感参数,通过最小二乘拟合建立土壤重金属全量反演模型。结果显示Cd、Pb与大部分植被指数存在显著负相关,基于NDVI(R1000,R670)的二次拟合模型能够有效地预测Cd全量,Pb估算模型以基于DVI(R1030-R670)的线性模型为最佳,表明重金属污染下的水稻冠层光谱变化特征能够获取土壤重金属含量信息,可监测耕地土壤重金属污染状况。     

新疆棉花LAI和叶绿素密度的高光谱估算研究

利用非成像高光谱仪,对棉花(2品种4水平种植密度)冠层5个关键生育时期进行光谱测定,分析棉花反射光谱及微分光谱生育期的变化规律,并对棉花冠层叶面积指数(LAI)、叶绿素密度(CH.D)与光谱数据进行回归分析,结果表明,用归一化差值植被指数(NDVI)与LAI建立的对数模型能够较好地估测棉花冠层的LAI(r=0.9123**,n=20);近红外729 nm波段处一阶微分光谱数值与CH.D高度相关(r=0.9372**,n=20),用此波段建立的CH.D估算模型,精度达84.3%,标准差为0.234g.m-2,RMSE=0.1569。研究表明,可以用高光谱数据对新疆棉花冠层LAI和CH.D进行遥感估算。     

Mapping leaf area index over a mixed natural forest area in the flooding season using ground-based measurements and Landsat TM imagery

Leaf area index (LAI) is an important structural parameter in terrestrial ecosystem modelling and management. Therefore, it is necessary to conduct an investigation on using moderate-resolution satellite imagery to estimate and map LAI in mixed natural forests in southeastern USA. In this study, along with ground-measured LAI and Landsat TM imagery, the potential of Landsat 5 TM data for estimating LAI in a mixed natural forest ecosystem in southeastern USA was investigated and a modelling method for mapping LAI in a flooding season was developed. To do so, first, 70 ground-based LAI measurements were collected on 8 April 2008 and again on 1 August 2008 and 30 July 2009; TM data were calibrated to ground surface reflectance. Then univariate correlation and multivariate regression analyses were conducted between the LAI measurement and 13 spectral variables, including seven spectral vegetation indices (VIs) and six single TM bands. Finally, April 08 and August 08 LAI maps were made by using TM image data, a multivariate regression model and relationships between April 08 and August 08 LAI measurements. The experimental results indicate that Landsat TM imagery could be used for mapping LAI in a mixed natural forest ecosystem in southeastern USA. Furthermore, TM4 and TM3 single bands (R ² > 0.45) and the soil adjusted vegetation index, transformed soil adjusted vegetation index and non-linear vegetation index (R ² > 0.64) have produced the highest and second highest correlation with ground-measured LAI. A better modelling result (R ²?=?0.78, accuracy?=?73%, root mean square error (RMSE)?=?0.66) of the 10-predictor multiple regression model was obtained for estimating and mapping April 08 LAI from TM data. With a linear model and a power model, August 08 LAI maps were successfully produced from the April 08 LAI map (accuracy?=?79%, RMSE?=?0.57), although only 58–65% of total variance could be accounted for by the linear and non-linear models.     

Influence of acquisition time and resolution on wheat yield estimation at the field scale from canopy biophysical variables retrieved from SPOT satellite data

Detailed information about the prediction of within-field potential in terms of yield at the field scale is an attractive goal that would allow useful applications in precision agriculture. Biophysical variables characterizing crop canopies, such as the leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FAPAR), fractional ground cover (Fcover) and the concentration of chlorophyll-a and -b (Cab), can be estimated from satellite remote-sensing data through the application of a neural network inversion of a radiative transfer model, such as PROSAIL. The knowledge of the temporal and spatial variability of these variables can enhance the possibilities of estimating yield at the field scale. The aim of this study is to investigate the influence of acquisition time and spatial resolution of biophysical variables estimated from satellite data on the grain yield estimation of wheat crops. We used SPOT 4 (spatial resolution: 20 m) and SPOT 5 (spatial resolution: 10 m) images, acquired at six different dates during the wheat growing season in 2012, to obtain LAI, Fcover, FAPAR, and Cab on five fields in Maccarese (Central Italy). A preliminary survey was carried out to correlate spatially biophysical variables with the final grain yield at each acquisition date. Biophysical variables estimated at a spatial resolution of 10 m during the stem elongation stage showed the best simple and spatial correlation with yield. At this stage, all the biophysical variables showed the highest correlation values as compared to the other dates. Subsequently, we used the variables estimated from SPOT data at each growth stage to calibrate multiple linear regression (MLR) and cubist regression (CR) models for two fields, which were then validated on five independent fields. Although the CR calibration models provided better accuracy than MLR, the best validation statistics were gained from MLR models, obtaining a root mean square error (RMSE) of about 1 t ha^?1 for three of these fields, using remote data having a spatial resolution of 10 metres and acquired between steam elongation and booting stage. The optimal acquisition time is affected, ceteris paribus, by the agricultural management and in particular by the variety that can influence the trend of crop growth. However, the optimal growth stage for yield estimation seems to be quite similar over the study area during a growth season. The validation of models on field data collected in another growing season is mainly affected by the climate conditions. These results highlight the importance of spatial resolution and the influence of acquisition time of satellite images on the estimation of yield at the field scale by remote-sensing data.     

Estimating aboveground tree biomass and leaf area index in a mountain birch forest using ASTER satellite data

Biomass and leaf area index (LAI) are important variables in many ecological and environmental applications. In this study, the suitability of visible to shortwave infrared advanced spaceborne thermal emission and reflection radiometer (ASTER) data for estimating aboveground tree and LAI in the treeline mountain birch forests was tested in northernmost Finland. The biomass and LAI of the 128 plots were surveyed, and the empirical relationships between forest variables and ASTER data were studied using correlation analysis and linear and non‐linear regression analysis. The studied spectral features also included several spectral vegetation indices (SVI) and canonical correlation analysis (CCA) transformed reflectances. The results indicate significant relationships between the biomass, LAI and ASTER data. The variables were predicted most accurately by CCA transformed reflectances, the approach corresponding to the multiple regression analysis. The lowest RMSEs were 3.45 t ha^?1 (41.0%) and 0.28 m²m^?2 (37.0%) for biomass and LAI respectively. The red band was the band with the strongest correlation against the biomass and LAI. SR and NDVI were the SVIs with the strongest linear and non‐linear relationships. Although the best models explained about 85% of the variation in biomass and LAI, the undergrowth vegetation and background reflectance are likely to affect the observed relationships.     

Spectral estimation of Green leaf area index of oats

Green leaf area index (LAI) is a measure of vegetative growth and development and is frequently used as an input parameter in yield estimation and evapotranspiration models. Extensive destructive sampling is usually required to achieve accurate estimates of green LAI in natural situations. In this investigation, a statistical modeling approach was used to predict the green LAI of oats from bidirectional reflectance data collected with multiband radiometers. Stepwise multiple regression models based on two sets of spectral reflectance factors accounted for 73% and 65% of the variance in green LAI of oats. Exponential models of spectral data transformations of greenness, normalized difference, and near-infrared/red ratio accounted for more of the variance in green LAI than the multiple regression models.     

An improved strategy for regression of biophysical variables and Landsat ETM+ data

Empirical models are important tools for relating field-measured biophysical variables to remote sensing data. Regression analysis has been a popular empirical method of linking these two types of data to provide continuous estimates for variables such as biomass, percent woody canopy cover, and leaf area index (LAI). Traditional methods of regression are not sufficient when resulting biophysical surfaces derived from remote sensing are subsequently used to drive ecosystem process models. Most regression analyses in remote sensing rely on a single spectral vegetation index (SVI) based on red and near-infrared reflectance from a single date of imagery. There are compelling reasons for utilizing greater spectral dimensionality, and for including SVIs from multiple dates in a regression analysis. Moreover, when including multiple SVIs and/or dates, it is useful to integrate these into a single index for regression modeling. Selection of an appropriate regression model, use of multiple SVIs from multiple dates of imagery as predictor variables, and employment of canonical correlation analysis (CCA) to integrate these multiple indices into a single index represent a significant strategic improvement over existing uses of regression analysis in remote sensing.To demonstrate this improved strategy, we compared three different types of regression models to predict LAI for an agro-ecosystem and live tree canopy cover for a needleleaf evergreen boreal forest: traditional (Y on X) ordinary least squares (OLS) regression, inverse (X on Y) OLS regression, and an orthogonal regression method called reduced major axis (RMA). Each model incorporated multiple SVIs from multiple dates and CCA was used to integrate these. For a given dataset, the three regression-modeling approaches produced identical coefficients of determination and intercepts, but different slopes, giving rise to divergent predictive characteristics. The traditional approach yielded the lowest root mean square error (RMSE), but the variance in the predictions was lower than the variance in the observed dataset. The inverse method had the highest RMSE and the variance was inflated relative to the variance of the observed dataset. RMA provided an intermediate set of predictions in terms of the RMSE, and the variance in the observations was preserved in the predictions. These results are predictable from regression theory, but that theory has been essentially ignored within the discipline of remote sensing.     

Discrete return lidar-based prediction of leaf area index in two conifer forests

Leaf area index (LAI) is a key forest structural characteristic that serves as a primary control for exchanges of mass and energy within a vegetated ecosystem. Most previous attempts to estimate LAI from remotely sensed data have relied on empirical relationships between field-measured observations and various spectral vegetation indices (SVIs) derived from optical imagery or the inversion of canopy radiative transfer models. However, as biomass within an ecosystem increases, accurate LAI estimates are difficult to quantify. Here we use lidar data in conjunction with SPOT5-derived spectral vegetation indices (SVIs) to examine the extent to which integration of both lidar and spectral datasets can estimate specific LAI quantities over a broad range of conifer forest stands in the northern Rocky Mountains. Our results show that SPOT5-derived SVIs performed poorly across our study areas, explaining less than 50% of variation in observed LAI, while lidar-only models account for a significant amount of variation across the two study areas located in northern Idaho; the St. Joe Woodlands (R² = 0.86; RMSE = 0.76) and the Nez Perce Reservation (R² = 0.69; RMSE = 0.61). Further, we found that LAI models derived from lidar metrics were only incrementally improved with the inclusion of SPOT 5-derived SVIs; increases in R² ranged from 0.02–0.04, though model RMSE values decreased for most models (0–11.76% decrease). Significant lidar-only models tended to utilize a common set of predictor variables such as canopy percentile heights and percentile height differences, percent canopy cover metrics, and covariates that described lidar height distributional parameters. All integrated lidar-SPOT 5 models included textural measures of the visible wavelengths (e.g. green and red reflectance). Due to the limited amount of LAI model improvement when adding SPOT 5 metrics to lidar data, we conclude that lidar data alone can provide superior estimates of LAI for our study areas.     

Using multiple radiometric correction images to estimate leaf area index

Ecological applications of remote-sensing techniques are generally limited to images after atmospheric correction, though other radiometric correction data are potentially valuable. In this article, six spectral vegetation indices (VIs) were derived from a SPOT 5 image at four radiometric correction levels: digital number (DN), at-sensor radiance (SR), top of atmosphere reflectance (TOA) and post-atmospheric correction reflectance (PAC). These VIs include the normalized difference vegetation index (NDVI), ratio vegetation index (RVI), slope ratio of radiation curve (K), general radiance level (L), visible-infrared radiation balance (B) and band radiance variation (V). They were then related to the leaf area index (LAI), acquired from in situ measurement in Hetian town, Fujian Province, China. The VI–LAI correlation coefficients varied greatly across vegetation types, VIs as well as image radiometric correction levels, and were not surely increased by image radiometric corrections. Among all 330 VI–LAI models established, the R ² of multi-variable models were generally higher than those of the single-variable ones. The independent variables of the best VI–LAI models contained all VIs from all radiometric correction levels, showing the potentials of multi-radiometric correction images in LAI estimating. The results indicated that the use of VIs from multiple radiometric correction images can better exploit the capabilities of remote-sensing information, thus improving the accuracy of LAI estimating.     

Albedo and LAI estimates from FORMOSAT-2 data for crop monitoring

This paper aimed at estimating albedo and Leaf Area Index (LAI) from FORMOSAT-2 satellite that offers a unique source of high spatial resolution (eight meters) images with a high revisit frequency (one to three days). It mainly consisted of assessing the FORMOSAT-2 spectral and directional configurations that are unusual, with a single off nadir viewing angle over four visible-near infra red wavebands. Images were collected over an agricultural region located in South Eastern France, with a three day frequency from the growing season to post-harvest. Simultaneously, numerous ground based measurements were performed over various crops such as wheat, meadow, rice and maize. Albedo and LAI were estimated using empirical approaches that have been widely used for usual directional and spectral configurations (i.e. multidirectional or single nadir viewing angle over visible-near infrared wavebands). Two methods devoted to albedo estimation were assessed, based on stepwise multiple regression and neural network (NNT). Although both methods gave satisfactory results, the NNT performed better (relative RMSE = 3.5% versus 7.3%), especially for low vegetation covers over dark or wet soils that corresponded to albedo values lower than 0.20. Four approaches for LAI estimation were assessed. The first approach based on a stepwise multiple regression over reflectances had the worst performance (relative RMSE = 65%), when compared to the equally performing NDVI based heuristic relationship and reflectance based NNT approach (relative RMSE ≈ 34%). The NDVI based neural network approach had the best performance (relative RMSE = 27.5%), due to the combination of NDVI efficient normalization properties and NNT flexibility. The high FORMOSAT-2 revisit frequency allowed next replicating the dynamics of albedo and LAI, and detecting to some extents cultural practices like vegetation cuts. It also allowed investigating possible relationships between albedo and LAI. The latter depicted specific trends according to vegetation types, and were very similar when derived from ground based data, remotely sensed observations or radiative transfer simulations. These relationships also depicted large albedo variabilities for low LAI values, which confirmed that estimating one variable from the other would yield poor performances for low vegetation cover with varying soil backgrounds. Finally, this empirical study demonstrated, in the context of exhaustively describing the spatiotemporal variability of surface properties, the potential synergy between 1) ground based web-sensors that continuously monitor specific biophysical variables over few locations, and 2) high spatial resolution satellite with high revisit frequencies.     

The relationship between the leaf area index (LAI) of rice and the C‐band SAR vertical/horizontal (VV/HH) polarization ratio

We investigated the relationship between the leaf area index (LAI) of rice and the ENVISAT Advanced Synthetic Aperture Radar (ASAR) vertical/horizontal (VV/HH) polarization ratio. Four alternating polarization ASAR images of swaths IS4 and IS5 over rice fields were used in the study. The VV/HH polarization ratio correlates well with the field‐measured LAI and an empirical relationship was established to estimate the LAI of rice using the VV/HH polarization ratio. A theoretical radiative transfer model was adopted to analyse the relationship. The error of the estimated LAI was 0.17 for the test site and a better correlation was found when LAI was less than 3.5. The results suggest that ASAR alternating polarization data can be used to estimate the LAI of rice for wide‐area monitoring of rice growth.     

基于Sas的时间序列缺失值处理方法比较

对于时间序列挖掘过程中的缺失值处理,目前有许多方法。在处理数据变量成一定的相关的数据集时,回归模型不失为较好的插补方法。利用均值插补、一元线性回归、多元线性回归、迭代回归方法对水文时间序列数据集的缺失数据进行处理,比较不同的皮氏相关系数下各方法的优劣及适用性。文中研究表明当数据集中存在与缺值变量相关度较大的变量时,一元线性回归的插补简单直观,且有较高的精度,结果接近真实;当数据集中不存在与缺值变量显著相关的自变量时,一元线性回归的结果变差,多元线性回归与多元迭代回归具有较好的结果,但多元迭代回归迭代次数难以确定,插补代价较大,多元线性回归为最佳选择;当缺值变量与其他自变量相关系数均较小时,回归插补的结果不理想,此时可考虑其他插补方法。