基于小口径惯性测量单元的管道中心线坐标解算方法

为提高在役管道惯性测量坐标数据的解算精度、高质量地推动管道数字化进程,基于惯性测量设备与里程计信息,结合平滑滤波算法,提出了一种针对小口径惯性测量单元的航位推算误差计算模型。通过状态方程、量测方程进行滤波递推解算,得到系统误差的最优估计值,并通过平滑滤波算法解算整周模糊度以保证数据精度。经过对管道惯性测量结果的验证,采用航位推算误差计算模型解算的管道中心线坐标与通过GNSS-RTK采集的中心线坐标数据相吻合,误差可以控制在米级。结果表明:基于惯性测量设备与里程计信息,结合平滑滤波算法,所建立的小口径惯性测量单元的航位推算误差计算模型能够有效减小纯惯性导航误差随时间累积对测量结果精度的影响,具有较好的应用价值。(图3,参21)     

基于PenmanMonteith模型的林木日蒸腾模拟

忽略大气层结,考虑气压订正,用冠层整体气孔阻力(rst)代换冠层阻力(rc),蒸散面的净辐射值(Rn)采用冠层截留净辐射(Rnl),便可在叶面积指数(LAI)、林木单叶平均气孔阻力(rsi)和气象要素实测数据的基础上,应用修正后的PenmanMonteith模型进行林木蒸腾量的模拟。本研究通过对Rn、LAI、rsi的实地观测,确定了林冠截流净辐射(Rnl)、消光系数(k)、冠层阻力转换系数(K')、空气动力学阻力(ra)和冠层整体气孔阻力(rst),对青海大通地区紫果云杉(Picea purpurea)、华北落叶松(Larix principisrupprechtii)、沙棘(Hippophae rhamnoides)、白桦(Betula platyphylla)和青杨(Populus cathayana)的日蒸腾过程进行了模拟,与用快速称重法订正的Li1600稳态气孔仪实测蒸腾结果对比,模拟的相对误差在±15%以内;模型敏感性分析发现,温度、LAI以及rsi是决定模拟结果的主要参数,模型对各参数变化反应不敏感。西北林学院学报21卷第3期刘胜等基于PenmanMonteith模型的林木日蒸腾模拟     

稠稀油黏度比对混合油黏度预测准确性的影响

稠油掺稀后混合油黏度的准确计算是稠油地面设施及管道工艺计算的基础,为了研究高黏度比(大于1×104)对于混合油黏度预测准确性的影响,首先筛选6个较为常用的混合油黏度计算模型,通过Capella稠油与4种不同物性稀油的现场掺稀测试,对38组共114个混合油黏度实测值和模型预测值进行对比,使用误差分析方法讨论了稠稀油黏度比对不同模型预测准确性的影响。研究表明:黏度比小于3 000时,Cragoe修正模型预测平均误差小于10%;黏度比大于3 000时,推荐采用Lederer改进模型,平均预测误差小于15%;黏度比对于混合油黏度模型预测误差的影响不可忽略,如需对现有模型进行修正,则应在修正式中引入黏度比。     

基于物品移动的局内装箱算法的改进

论述了内燃机缸内压缩形式随着燃烧室几何形状的不同而改变的观点,结合燃烧室主要几何尺寸参数采用数值计算方法对缸内压缩形式进行插值计算,改进已有k - ε湍流模型中体现压缩性的系数,从而修正了缸内k - ε湍流模型.进一步将修正后的模型应用于一内燃机缸内湍流数值计算中,得到的计算结果与标准k - ε模型计算结果以及实验结果进行了比较.结果表明:修正后的湍流k - ε模型模拟精确性有所提高,适用于计算内燃机缸内湍流运动.     

三分量磁通门传感器的三轴正交校正与测量

受机械加工与安装水平的限制,磁通门传感器的三个分量相互之间无法保证绝对正交,由此会带来三分量磁测数据误差。本文通过建立一种三轴正交角度与磁总场误差关系模型,并对该模型进行正演模拟、反演分析及建立一种修正算法,达到推算三个分量不正交角度并修正的目的。实验表明,采用该方法对实际三分量磁通门传感器进行不正交度的测试与修正后,能够使其在测量总场时的误差减小到±10 n T,减小了测量时所带来的误差。     

东北丘陵半干旱区参考作物蒸散量计算方法比较

利用FAO Penman-Monteith公式、FAO Penman修正式和Priestley-Taylor公式对东北丘陵半干旱区观测到的气象数据进行了逐日参考作物蒸散量计算.结果显示,FAO Penman修正式的计算值比FAO Penman-Monteith公式的计算值平均偏大约16%,2种比较方法具有很好的相关性;而Priestley-Taylor公式的计算值与FAO Penman-Monteith公式的计算值相比,差异比较显著,是由于Priestley-Taylor公式没有考虑空气动力项对参考作物蒸散量的影响.因此,在东北丘陵半干旱区使用Priestley-Taylor公式计算参考作物蒸散量,必须根据不同月份对公式中的常数项重新进行修正.     

基于精确喷雾的水平叶面积指数检测

果树的叶面积指数(LAI)能为精确喷雾提供重要参考依据,但常规定义从垂直方向检测叶面积指数不满足水平方向精确喷雾的实际需求。采用LAI近红外透射检测系统从水平方向上检测柑橘树的LAI值,进而计算水平方向的叶片总面积,并以应用直接法得到的叶片真实总面积作为标准值衡量误差大小,同时与垂直方向的检测结果比较。结果表明:在水平方向上东西和南北2个方向LAI检测结果的绝对误差的平均绝对值分别为0.45和0.40,相对误差的平均绝对值分别为17.37%和14.91%;叶片总面积的检测结果的误差与在垂直方向上应用LAI红外透射检测系统或WinSCANOPY型冠层分析仪的检测结果的误差接近;在水平方向上检测LAI和叶片总面积的准确性能够满足试验要求。     

宁波地区参考作物蒸散量计算模型研究

针对宁波地区节水灌溉中需要动态调节问题,研究参考作物蒸散量(ET0)在气象资料短缺条件下不同类型的简化计算方法,运用FAO-56 Penman-Monteith(PM)法、FAO-24 PM法、Hargreaves法、Mc Cloud法、PriestleyTaylor法和Makkink法计算宁波鄞县站1 971—2015年逐日的ET0。结果表明,Hargreaves法和Makkink法计算误差较小,相关性显著,Mc C loud法计算误差较大。通过总ET0值分析,相对误差都较大,在15%以上,这些方法在宁波地区适用需进行修正研究。本文对显著相关性的Hargreaves和Makkink进行修改,改进后模型相关性显著,且相关误差非常小,接近于0。得出这2个模型可以作为宁波地区气象资料短缺和气温异常波动双重背景下ET0的简化计算方法。     

河西绿洲灌区小麦灌溉预报模型的研究

采用土壤含水量的平衡计算模型和土壤水分指数,结合气象资料,对河西绿洲灌区小麦实际蒸散量和农田土壤水分动态变化进行了模拟分析.结果表明,实际观测值与Penman-Monteith公式修正计算值之间误差比较小,水面蒸发量与作物蒸散量的数学模拟之间的线性关系很明显,相关性很高.因此,该模型可以用于灌水时间的预报.     

多通道湿度信号采集器示值误差的修正及业务应用

多通道湿度信号采集器将湿度传感器的输出电压转换为相对湿度数值,这一模数转换过程的准确性决定了检定数据的准确性。目前,自动气象站检定规程中没有给出多通道采集器的溯源方法。为了保证湿度传感器检定的准确性,对采集器进行比对试验,通过示值误差计算出修正值。在数据处理过程中,采用该修正值消除多通道采集器的误差。为了进一步增质提效,设计了湿度传感器误差计算软件代替人工计算,彻底消除人为计算误差。     

基于PyWOFOST作物模型的东北玉米估产及精度评估

【目的】构建合理的作物估产方案,提高作物估产精度。【方法】本文以基于集合卡尔曼滤波（Ensemble Kalman Filter,EnKF）构建的遥感信息-作物模型结合模型（PyWOFOST）为基础,建立了以LAI为结合点,适用于中国东北地区玉米的同化模拟模型,并使用MODIS LAI数据作为外部同化数据进行同化模拟,重点分析了遥感观测（MODIS LAI）和模型参数（出苗-开花期所需积温,TSUM1）的不确定性（即随机误差）对同化模拟结果的影响。最后,利用PyWOFOST模型实现了区域尺度上的玉米估产。【结果】同化外部观测数据后的玉米模拟产量较未同化外部数据的模拟产量有明显改善,20个未受灾害影响的农气站玉米产量同化前的模拟误差及在TSUM1的不确定性为0、10、20、30℃时的同化后模拟误差分别为14.04%、12.71%、11.91%、10.44%及10.48%;同化后的模拟LAI普遍较同化前的模拟LAI更接近实测LAI,更符合玉米LAI的变化趋势;同化前模拟发育期与实测发育期平均绝对误差为3.4 d,而同化后在TSUM1的不确定性为0、10、20、30℃时模拟发育期与实测发育期的平均误差分别为3.5、4.3、5.0、5.5 d。区域尺度上玉米估产结果表明,58.82%的区域玉米估产误差在15%以内,同化产量和统计产量的确定系数为0.806。【结论】基于集合卡尔曼滤波同化遥感信息进行作物估产是可行的。     

Comparative analysis of GF-1,HJ-1,and Landsat-8 data for estimating the leaf area index of winter wheat

Using simultaneously collected remote sensing data and field measurements,this study firstly assessed the consistency and applicability of China high-resolution earth observation system satellite 1(GF-1) wide field of view(WFV) camera,environment and disaster monitoring and forecasting satellite(HJ-1) charge coupled device(CCD),and Landsat-8 operational land imager(OLI) data for estimating the leaf area index(LAI) of winter wheat via reflectance and vegetation indices(VIs). The accuracies of these LAI estimates were then assessed through comparison with an empirical model and the PROSAIL radiative transfer model. The effects of radiation calibration,spectral response functions,and spatial resolution on discrepancies in the LAI estimates between the different sensors were also analyzed. The results yielded the following observations:(1) The correlation between reflectance from different sensors is relative good,with the adjusted coefficients of determination(R2) between 0.375 to 0.818. The differences in reflectance are ranging from 0.002 to 0.054. The correlation between VIs from different sensors is high with the R2 between 0.729 and 0.933. The differences in the VIs are ranging from 0.07 to 0.156. These results show the three sensors' images can all be used for cross calibration of the reflectance and VIs.(2) The four VIs from the three sensors are all demonstrated to be highly correlated with LAI(R2 between 0.703 and 0.849). The linear models associated with the 2-band enhanced vegetation index(EVI2),which feature the highest R2(higher than 0.746) and the lowest root mean square errors(RMSE)(less than 0.21),were selected to estimate the winter wheat LAI. The accuracy of the estimated LAI from Landsat-8 was the highest,with the relative errors(RE) of 2.18% and an RMSE of 0.13,while the HJ-1 was the lowest,with the RE of 2.43% and the RMSE of 0.15.(3) The inversion errors in the different sensors' LAI estimates using the PROSAIL model are small. The accuracy of the GF-1 is the highest with the RE of 3.44%,and the RMSE of 0.22,whereas that of the HJ-1 is the lowest with the RE of 4.95%,and the RMSE of 0.26.(4) The effects of the spectral response function and radiation calibration for the different sensors are small and can be ignored,but the effects of spatial resolution are significant and must be taken into consideration in practical applications.     

A comparison of three methods for estimating leaf area index of paddy rice from optimal hyperspectral bands

This paper follows previous research that identified 15 hyperspectral wavebands that were suitable to estimate paddy rice leaf area index (LAI). The objectives of the study were to: (1) test the efficiency of the wavebands selected in the previous study, (2) to evaluate the potential of least squares support vector machines (LS-SVM) to estimate paddy rice LAI from canopy hyperspectral reflectance and (3) to compare multiple linear regression-MLR, partial least squares-PLS regression and LS-SVM to determine paddy rice LAI using the selected wavebands. In the study, measurements of hyperspectral reflectance (350–2500 nm) and corresponding LAI were made for a paddy rice canopy throughout the growing seasons. On the basis of the wavebands selected previously, models based on MLR, PLS and LS-SVM to estimate rice LAI were compared using the data from 123 observations, which were split randomly for model calibration (2/3) and validation (1/3). Root mean square errors (RMSEs) and the correlation coefficients (r) between measured and predicted LAI values from model calibration and validation were calculated to evaluate the quality of the models. The results showed that the LS-SVM model using the 15 selected wavebands produced more accurate estimates of paddy rice LAI than the PLS and MLR models. We concluded that the LS-SVM approach may provide a useful exploratory and predictive tool for estimating paddy rice LAI when applied to reflectance data using the 15 selected wavebands.     

基于随机森林算法的冬小麦叶面积指数遥感反演研究

【目的】通过利用随机森林算法（random forest,RF）反演冬小麦叶面积指数（leaf area index, LAI）,及时、准确地监测冬小麦长势状况,为作物田间管理和产量估测等提供科学依据。【方法】本研究依据冬小麦拔节期、挑旗期、开花期及灌浆期地面观测数据,将相关系数分析（correlation coefficient,r）和袋外数据（out-of-bag data,OOB）重要性分析与随机森林算法（random forest,RF）相结合,在优选光谱指数和确定最佳自变量个数的基础上,构建了两种冬小麦LAI反演模型|r|-RF和OOB-RF,并利用独立数据集对两种模型进行验证;然后,将所建LAI反演模型用于无人机高光谱影像,进一步检验所建模型对无人机低空遥感平台的适用性和可靠性。【结果】|r|-RF和OOB-RF反演模型分别采用相关性前5强、重要性前2强的光谱指数作为输入因子时精度最优,验证决定系数（R²）分别为0.805、0.899,均方根误差（RMSE）分别为0.431、0.307,表明这两个模型均能对作物LAI进行精确反演,其中OOB-RF模型的反演效果更好。利用无人机高光谱影像数据结合OOB-RF估算模型反演得到冬小麦LAI与地面实测值的拟合方程的决定系数R²为0.761,RMSE为0.320,数值范围（1.02-6.41）与地面实测（1.29-6.81）亦比较吻合。【结论】本文基于地面数据构建的OOB-RF模型不仅具有较高的反演精度,而且适用性强,可用于无人机高光谱遥感平台提取高精度的冬小麦LAI信息。     

不同叶面积指数遥感产品在浙江省的差异比较研究

本文比较分析了三种基于MODIS数据生成的LAI遥感产品(MOD15 LAI、MCD15 LAI和GLASS LAI)在浙江省的差异;以地面观测的LAI结合TM遥感数据生成的30 m分辨率LAI数据(LAITM)为参考,评价了它们在浙江省天童山常绿阔叶林地区的可靠性。研究发现,三种LAI遥感产品在可靠性、大小、空间格局及变化趋势上均存在显著差异:GLASS LAI表现优于MOD15 LAI和MCD15 LAI,GLASS LAI与LAITM具有较好的相关性(R2=0.61,RMSE=1.20),而MOD15 LAI和MCD15 LAI与LAITM的一致性较差(RMSE分别为1.42和1.63)。2003—2012年期间,基于GLASS LAI得到的浙江省LAI年平均值(2.13 m2/m2)分别高出MOD15 LAI和MCD15 LAI约23%和12%;而GLASS LAI年最大值的10年平均值(3.82 m2/m2)比MOD15 LAI和MCD15 LAI的值都偏低约30%。2003—2012年期间,GLASS LAI年平均值在浙江省显著升高和下降的面积比例分别为16.6%和14.7%;MOD15 LAI和MCD15 LAI分别在全省21.8%和13.7%的地区呈明显下降趋势,明显升高地区分别仅占1.2%和4.0%。三种LAI遥感产品的全省年平均值方面,GLASS LAI具有较大的年际波动,但趋势不明显;MCD15 LAI略有下降;而MOD15 LAI呈现较为明显的下降趋势(0.02 m2/m2,P=0.06)。     

典型阔叶红松林叶面积指数的空间异质性

目的探讨叶面积指数(LAI)的空间异质性及其影响因素，以期为准确地获得局域、区域等大尺度上LAI的空间分布特征提供参考依据。方法依托黑龙江丰林国家级自然保护区30hm2典型阔叶红松林动态监测样地，首先通过LAI与胸高断面积间的经验模型得到阔叶红松林内红松、冷杉、紫椴、硕桦、裂叶榆和色木槭6种主要树种及林分水平上的LAI，然后采用半方差函数和Kriging空间插值等地统计学方法分析LAI的空间异质性特征以及与地形因子(海拔、坡度、坡向和曲率)的相关关系。结果主要树种LAI的变异系数均大于10%，具有中等或强变异性，且变异程度表现为裂叶榆>硕桦>紫椴>红松>色木槭>冷杉。红松LAI的空间结构比(块金值(C₀)/基台值(C₀+C))为0.50，具有中等程度的空间自相关，而其他5个树种的比值均低于0.25，具有强烈的空间自相关；主要树种LAI的变程范围为24m(紫椴)~126m(红松)。红松、裂叶榆和色木槭的空间异质性具有较明显的各向异性结构特点，且红松的LAI在240m尺度范围内时，东西方向(0°)上的空间异质性明显大于南北方向(90°)，240m以后出现相反的结果。红松的LAI与海拔、坡度、坡向和曲率4个地形因子均呈极显著正相关(P＜0.01)，其他树种的LAI与地形因子也表现出不同的相关关系。结论LAI的空间异质性不仅与研究尺度相关，而且与方向相关；地形因子对LAI空间分布的影响存在种间差异，但整体来看，4个地形因子均对LAI的空间分布存在显著影响。      

基于分段方式选择敏感植被指数的冬小麦叶面积指数遥感反演

【目的】考虑到利用单一植被指数（VI）反演叶面积指数(LAI)时,存在着不同程度的饱和性和易受土壤背景影响的问题,提出通过分段的方式选择敏感植被指数形成最佳VI组合以提高LAI反演的精度。【方法】通过ACRM辐射传输模型模拟数据,结合地面实测光谱数据,选择常用的植被指数进行土壤敏感性分析以及饱和性分析确定LAI的分段点,并在此基础上分段选择最佳植被指数形成组合VI来实现LAI的最终反演,并利Landsat5 TM开展区域条件下冬小麦LAI反演应用。【结果】以LAI=3是较为适宜的分段点,利用植被指数最佳分段组合OSAVI(LAI≤3)+TGDVI(LAI>3)可在一定程度上有效克服土壤影响因素以及饱和性问题,联合反演的结果明确优于单一植被指数反演精度。【结论】通过分段选择最佳植被指数形成联合VI可以有效提高LAI反演精度。     

Winter wheat yield estimation based on assimilated Sentinel-2 images with the CERES-Wheat model

Assimilating Sentinel-2 images with the CERES-Wheat model can improve the precision of winter wheat yield estimates at a regional scale. To verify this method, we applied the ensemble Kalman filter(EnKF) to assimilate the leaf area index(LAI) derived from Sentinel-2 data and simulated by the CERES-Wheat model. From this, we obtained the assimilated daily LAI during the growth stage of winter wheat across three counties located in the southeast of the Loess Plateau in China: Xiangfen, Xinjiang, and Wenxi. We assigned LAI weights at different growth stages by comparing the improved analytic hierarchy method, the entropy method, and the normalized combination weighting method, and constructed a yield estimation model with the measurements to accurately estimate the yield of winter wheat. We found that the changes of assimilated LAI during the growth stage of winter wheat strongly agreed with the simulated LAI. With the correction of the derived LAI from the Sentinel-2 images, the LAI from the green-up stage to the heading–filling stage was enhanced, while the LAI decrease from the milking stage was slowed down, which was more in line with the actual changes of LAI for winter wheat. We also compared the simulated and derived LAI and found the assimilated LAI had reduced the root mean square error(RMSE) by 0.43 and 0.29 m2 m–2, respectively, based on the measured LAI. The assimilation improved the estimation accuracy of the LAI time series. The highest determination coefficient(R2) was 0.8627 and the lowest RMSE was 472.92 kg ha–1 in the regression of the yields estimated by the normalized weighted assimilated LAI method and measurements. The relative error of the estimated yield of winter wheat in the study counties was less than 1%, suggesting that Sentinel-2 data with high spatial-temporal resolution can be assimilated with the CERES-Wheat model to obtain more accurate regional yield estimates.     

基于高光谱的油菜叶面积指数估计

以冬油菜为研究对象,2014-2015年度设计了不同施氮水平直播油菜小区试验,在不同生育时期测量冠层光谱、土壤背景光谱以及叶面积指数(leaf area index,LAI),通过相关分析选取了12个光谱特征参数和11个植被指数,建立6叶期至角果期LAI的5种线性和非线性定量反演模型。结果表明:二次多项式反演模型比较适合估算油菜LAI苗期时以红边参数为代表的光谱特征参数,可准确估算出LAI;6叶期时红边幅值预测模型R~2为0.81,RMSEP为0.39,RPD为1.62;8叶期时红蓝边面积比归一化预测模型R~2为0.79,RMSEP为0.60,RPD为2.30;10叶期时红边幅值预测模型R~2为0.92,RMSEP为0.47,RPD为2.36;盛花期时蓝边面积预测模型R~2为0.87,RMSEP为0.34,RPD为2.57;角果期时以RDVI为代表的植被指数也可准确估算出LAI,预测模型R~2为0.74,RMSEP为0.57,RPD为1.36。油菜全生育期采用相同光谱特征参数、植被指数建模估计LAI精度明显降低,预测R~2远小于0.75,RMSEP大于0.65,RPD值均小于1.40,表明难以采用统一参数建模准确估计油菜全生育期LAI,不同生长时期需选择合适的光谱参数、植被指数分段建模估计LAI。     

Exploitation of spatial information in high resolution digital imagery to map leaf area index

Crop responses to management practices and the environment, as quantified by leaf area index (LAI), provide decision-making criteria for the delineation of crop management zones. The objective of this work was to investigate whether spatial correlations inferred from remotely sensed imagery can be used to interpolate and map LAI using a relatively small number of ground-based LAI measurements. Airborne imagery was recorded with the Airborne Imaging Spectrometer for Applications (AISA) radiometer over a 3.2 ha corn field. Spectral vegetation indexes (SVI) were derived from the image and aggregated to cells of 2 × 2 m², 4 × 4 m², and 8 × 8 m² resolution. The residual maximum likelihood method was used to estimate the LAI variogram parameters. A generalized least squares regression was used to relate ground truth LAI data and collocated image pixels. This regression result was then used to convert variograms from the imagery to LAI units as well as to interpolate and map LAI. The decrease in resolution by merging pixels led to an increase in the value of the r ² and to a decrease in root mean-squared error (RMSE) values. The accuracy of kriged estimates from the variogram of the measured LAI and that from the image derived variograms was estimated by cross-validation. There was no difference in the accuracy of the estimates using either variograms from measured LAI values or from those of converted SVIs. Maps of LAI from ground-based measurements made by kriging the data with image-derived variogram parameters were similar to those obtained by with kriging with the variogram of measured LAI. Similar coarse spatial trends of high, medium and low LAI were evident for both maps. Variogram parameters from ground-based measurements of LAI compared favorably with those derived from remotely sensed imagery and could be used to provide reasonable results for the interpolation of LAI measurements.