基于光谱变换的滨海湿地土壤全氮含量建模预测          下载免费PDF全文

张清文  吴风华  宋敬茹  汪金花  张永彬  刘明月  李孟倩  李春景  郝玉峰  满卫东 《土壤》2023,55(4):880-886

基于133个滨海湿地土样的全氮（TN）含量和光谱反射率（R）及其对数（lgR）、对数的一阶微分（（lgR）''）、倒数（1/R）、倒数的一阶微分（（1/R）''）、一阶微分（R''）、平方根（√R）、一阶微分的倒数（1/（R）''）变换，采用偏最小二乘回归（PLSR）、随机森林回归（RFR）和支持向量机回归（SVR）3种算法分别建立土壤TN含量估测模型。结果表明：①土壤TN含量与光谱变换形式相关性由高到低为：（1/R）''> R''> （lgR）''> 1/R > lgR > 1/（R）''> √R > > R，经光谱变换，土壤TN含量与变换光谱的相关性均高于R，其中与（1/R）''的Pearson相关系数最大为0.746。②PLSR和SVR基于R''、（1/R）''、（lgR）''和1/（R）''变换构建的模型、RFR方法构建的所有模型R²均大于0.732，均可用于滨海湿地土壤TN含量的估算。③基于1/（R）''建立的SVR模型预测精度最高，其R²为0.987，RMSE为0.057 g/kg，MAE为0.050 g/kg，是预测滨海湿地土壤TN含量的最优模型，可为准确获取滨海湿地土壤TN含量提供稳定方法。  相似文献   

用偏最小二乘法及傅立叶变换近红外光谱快速检测白酒酒精度   总被引：1，自引：1，他引：1  

彭帮柱  龙明华  岳田利  袁亚宏  赵志华 《农业工程学报》2007,23(4):233-237

为了得到白酒工业中酒精度的快速检测技术，将偏最小二乘法与傅立叶变换近红外光谱相结合，通过解析白酒样品的近红外光谱图和对光谱进行不同的预处理，结果表明：用最大最小归一化法预处理光谱，光谱范围选择9747.1～7498.3 cm^-1和6102～5446.3 cm^-1，采用内部交叉验证建立模型，决定系数(R²)为99.99%，交互验证均方根差(RMSECV)为0.165%，主成分数为4，此条件下建模效果较好；模型进行验证结果表明预测集相关系数(R²)为99.80%，预测标准偏差(RMSEP)为0.264%，模型的预测效果很好，具有较高的精密度和良好的稳定性，能满足生产中白酒酒精度的快速检测要求。  相似文献   

利用无人机多光谱影像的多品种玉米成熟度监测     

姜友谊  刘博伟  张成健  赵丹  陈日强  徐波  龙慧灵  杨贵军  杨浩 《农业工程学报》2023,39(20):84-91

基于遥感监测多品种玉米成熟度进而掌握最佳收获时机，对提高其产量和品质至关重要。该研究在玉米成熟阶段获取无人机多光谱影像，同步采集叶片叶绿素含量（chlorophyll content，C）、籽粒含水率（moisture content，M）、乳线占比（proportion of milk line，P）等地面实测数据，以此构建玉米成熟度指数（maize maturity index，MMI），从而定量表征玉米成熟度。通过MMI与植被指数构建回归模型和随机森林模型，验证MMI适用性，并分析无人机遥感对不同品种玉米成熟度的监测精度。结果表明：1）不同品种玉米的叶片叶绿素含量、籽粒含水率、乳线占比的变化速率均存在差异。2）MMI与所选植被指数的相关性均可达到0.01显著水平，其中与归一化植被指数（normalized difference vegetation index，NDVI）、转换叶绿素吸收率（transformed chlorophyll absorbtion ratio index，TCARI）相关性最高，相关系数均为0.87。3）该研究基于不同组合的数据集进行了模型验证，其中随机森林模型对MMI的估测精度最高，测试集决定系数（coefficient of determination，R²）为0.84，均方根误差（root mean squared error，RMSE）为8.77%，标准均方根误差（normalized root mean squared error，nRMSE）为12.05%。此外，随机森林模型对不同品种MMI的估测精度较好，京九青贮16精度最优，其中R²、RMSE、nRMSE为0.76、10.67%、15.88%，模型精度证明了可以利用无人机平台对不同品种玉米成熟度进行监测。研究结果可为多光谱无人机实时监测农田多品种玉米成熟度的动态变化提供参考。  相似文献   

基于光谱指数与机器学习算法的土壤电导率估算研究   总被引：1，自引：0，他引：1  

曹肖奕  丁建丽  葛翔宇  王敬哲 《土壤学报》2020,57(3):867-877

土壤盐分是干旱区土壤盐渍化评价的重要指标。以新疆维吾尔自治区渭干河-库车河三角洲绿洲为例,基于土壤电导率 (Electrical conductivity,EC) 及可见光-近红外 (Visible and near infrared, VIS-NIR) 光谱数据,通过蒙特卡洛交叉验证 (Monte Carlo cross validation, MCCV) 确定364个有效样本。采用原始光谱 (Raw reflectance, R) 及其经过微分、吸光度 (Absorbance, Abs)、连续统去除 (Continuum removal, CR) 等6种预处理后的数据构建光谱指数。基于遴选出的21个最优指数,采用BP神经网络 (Back propagation neural network, BPNN)、支持向量机 (Support vector machine, SVM)、极限学习机 (Extreme learning machine, ELM) 三种算法对EC进行估算,并引入偏最小二乘回归 (Partial least squares regression, PLSR) 进行比较。结果表明：在基于R与6种光谱预处理数据构建的21个最优光谱指数之中,R_FD_RSI (R₁₉₁₃,R₂₁₄₂) 表现最佳 (r = 0.649) ;与PLSR相比,机器学习算法能够显著提高模型的估算精度,R²提高了34.55%。三种机器学习算法模型中,ELM表现最优 (R² = 0.884, RMSE = 3.071 mS?cm^-1, RPIQ = 2.535) 。本研究中所构建的光谱指数在兼顾遥感机理的同时能深度挖掘更多的隐含信息,并且基于机器学习算法的土壤EC估算模型精度显著提高,为干旱区土壤盐分定量估算提供了科学参考。  相似文献   

机器学习结合高光谱植被指数与SPAD值估算冬小麦氮含量     

冯惠芬  李映雪  吴芳  邹晓晨 《农业工程学报》2024,40(1):235-245

冬小麦叶片氮含量与叶片光合作用和营养状况密切相关,直接影响植株生长发育,而茎秆中的氮含量与茎秆中纤维素、半纤维素和木质素的比例和含量密切相关,直接影响茎秆质量及植株的抗倒伏能力。然而,有关对冬小麦茎秆氮含量估算研究较为有限,限制了从氮含量角度判断茎秆质量及对倒伏的预测能力。为精准估算冬小麦不同器官（叶片、茎秆）氮含量,该研究通过2年田间试验,获取冬小麦4个关键生育期（拔节期、抽穗期、开花期、灌浆期）和3种施氮水平条件下（N1、N2和N3）的冠层光谱反射率、叶片、茎秆氮含量及叶片SPAD (soil and plant analyzer development, SPAD)值。分析了不同生育期和施氮水平条件下高光谱植被指数对叶片和茎秆氮含量的敏感性,并结合5种常用的机器学习算法：随机森林回归（random forest regression,RFR）、支持向量回归（support vector regression,SVR）、偏最小二乘回归（partial least squares regression,PLSR）、高斯过程回归（gaussian process regression,GPR）、深度神经网络回归（deep neural networks,DNN）构建冬小麦叶片和茎秆氮含量估算模型。结果表明：高光谱植被指数对叶片和茎秆氮含量的敏感性受到生育期和施氮水平的影响。在灌浆期,最佳植被指数双峰冠层植被指数 DCNI（double-peak canopy nitrogen index）对叶片氮含量的敏感性最高,R²为0.866。对茎秆氮含量,在抽穗期的敏感性最高,最佳植被指数归一化叶绿素比值指数 NPQI（normalized phaeophytinization index）与氮含量相关系数R²＝0.677。施氮水平的提升增加了光谱植被指数对茎秆氮含量的敏感性。结合SPAD值的机器学习算法提升了氮含量的估算精度,对叶片氮含量,在不同生育期和施氮水平条件下估算精度提升了1%～7%,其中在全生育期的归一化均方根误差NRMSE从0.254提升到0.214,抽穗期的NRMSE提升最大,从0.201提升到0.128。对茎秆氮含量,全生育期的NRMSE从0.443提升到0.400,抽穗期的NRMSE提升最大,从0.323提升到0.268。在全生育期,结合SPAD值的DNN模型对叶片（R²=0.782、NRMSE=0.214）和茎秆（R²=0.802、NRMSE=0.400）氮含量的估算精度最佳。研究说明,SPAD值与光谱植被指数结合有利于提升冬小麦不同生育期和施氮水平条件下叶片和茎秆氮含量的估算精度。  相似文献   

近红外光谱分析法测定菜籽油中芥酸的含量   总被引：6，自引：0，他引：6  

陈蛋  陈斌  陆道礼  钟旭美 《农业工程学报》2007,23(1):234-237

采用多通道PDA型近红外光谱仪，应用偏最小二乘法建立了菜籽油中芥酸含量与近红外透射光谱的校正模型，讨论多项式求导及平滑的窗口宽度和相关系数法筛选有效波长对校正模型的影响，并对10个预测集样品利用预测相关系数R_p和预测均方根误差RMSEP指标进行了预测精度分析，结果发现：在使用全谱数据进行偏最小二乘回归建模时，一阶7点求导及平滑的预处理方法结果最佳，此时建模效果为：R_p=0.739，RMSEP=1.659；在此基础上通过相关系数法筛选波长后的建模效果为：R_p=0.958，RMSEP=0.963。后者R_p提高29%，RMSEP减少42%。由此可得出多项式求导及平滑法和相关系数法相结合对校正模型稳健性，预测精度都有较大提高的结论。研究证明：多通道近红外光谱仪快速测测菜籽油中芥酸含量的方法是可行的。  相似文献   

利用正交信号校正消除水分影响的银川平原土壤盐分高光谱反演          下载免费PDF全文

陈睿华  王怡婧  张俊华  丁启东  贾科利  许鑫杰 《农业工程学报》2023,39(19):122-130

高光谱遥感是监测土壤盐渍化的重要手段之一，但野外光谱反射率易受土壤水分的影响，导致盐分监测精度难以保证。为有效消除水分因素，提高土壤含盐量反演精度，该研究以银川平原盐渍化土壤为研究对象，以野外土壤光谱反射率（reflectance，Ref）和实测土壤含盐量为数据源，分析不同含水率的土壤光谱特征，将反射率经过一阶微分（first derivative of reflectance，FDR）、正交信号校正（orthogonal signal correction，OSC）和一阶微分-正交信号校正（first derivative of reflectance - orthogonal signal correction，FDR-OSC）变换，分析各光谱数据与含盐量、含水率的相关性，确定最佳“除水”方法，然后基于支持向量机（support vector machine，SVM）建立土壤含盐量反演模型。结果表明：1）含水率与土壤光谱反射率呈反比，光谱在1 430、1 950、2 200 nm附近存在吸收带，1 950 nm附近为最主要吸收波段，且存在向长波漂移的现象。2）光谱数据与含水率相关性由强到弱的顺序为：Ref、OSC、FDR、FDR-OSC；与含盐量相关性由强到弱的顺序为：FDR-OSC、FDR、OSC、Ref。3）基于FDR-OSC“除水”的SVM含盐量模型决定系数R_c²、R_p²和相对分析误差（relative prediction deviation，RPD）分别达到0.952、0.960和5.04，具有极强的拟合和反演能力。研究结果可为银川平原及同类地区土壤含盐量的精准监测提供科学依据。  相似文献   

大豆叶绿素含量高光谱反演模型研究   总被引：24，自引：4，他引：24  

宋开山  张柏  王宗明  刘焕军  段洪涛 《农业工程学报》2006,22(8):16-21

叶绿素是植物体进行光合作用、进行第一性生产的重要物质，能够间接反映植被的健康状况与光合能力，同时也能反映植被受环境胁迫后的生理状态。高光谱遥感为快速、大面积监测植被的叶绿素变化提供了可能。该研究实测了不同水肥耦合作用下，大豆冠层的高光谱反射率与叶绿素含量数据，对二者进行了相关分析；采用特定叶绿素敏感波段建立了植被指数叶绿素估算模型；最后采用相关系数较大的波段作为神经网络模型的输入变量进行了叶绿素含量的估算。经对比发现叶绿素A、B与光谱反射率在可见光与近红外波段的相关系数的变化趋势基本一致，在可见光谱波段呈负相关，近红外波段呈正相关，红边处相关系数由负变正。特定色素植被指数可以提高大豆叶绿素估算精度(R²>0.736)，但是人工神经网络模型可以大大提高大豆叶绿素含量的估算水平，当隐藏层节点数为4时，R²大于0.94，随着隐藏层节点数的增加，R²可高达0.99，表明神经网络模型可以大大提升高光谱反演大豆叶绿素含量的能力。  相似文献   

基于Sentinel-2卫星数据的灌区农田土壤水盐协同反演     

贺玉洁  张智韬  巴亚岚  杜瑞麒  殷皓原  项茹  吴雨箫 《农业工程学报》2023,39(19):111-121

土壤含水量（soil water content, SWC）和土壤含盐量（soil salt content, SSC）是影响作物生长和农业生产力的重要因素。光学卫星图像已成为SWC和SSC估计的主要数据源。然而,在SWC或SSC变化较大地区,土壤水分和盐分会影响对方对光谱反射率的响应,使得SSC和SWC的反演精度较差。对此,该研究提出了一个半解析性的反射率模型—RVS模型,来模拟植被光谱反射率（R_v）对作物根区土壤含水量和含盐量的响应;并通过构建的RVS模型,对植被覆盖区域的土壤含水量和土壤含盐量进行同步监测。研究表明：RVS模型在反演研究区土壤含盐量和含水量时,精度较为可靠(水分：决定系数R²为0.63～0.74,均方根误差为0.017～0.028;盐分：决定系数R²为0.68～0.75,均方根误差为0.0525～0.0617)。在作物生长过程中,植被光谱反射率对深层土壤的含水量和含盐量的响应比对浅层土壤的含水量和含盐量的响应更加明显,而且随着作物的生长,影响光谱反射率的主导因素从土壤水分慢慢转向土壤盐分和水盐相互作用。该研究在一定程度上揭示了土壤水分、盐分、水盐交互作用对作物光谱反射率的干扰过程,实现土壤水分和盐分的同步监测,对实现区域尺度上土壤含盐量和含水量的精准监测具有一定的意义。  相似文献   

基于三维光谱特征空间的干旱区土壤盐渍化遥感定量研究   总被引：5，自引：0，他引：5  

丁建丽  姚远  王飞 《土壤学报》2013,50(5):853-861

土壤盐渍化是干旱半干旱区农业发展的重要制约因素,同时也是干旱区所面临的最主要的生态环境问题之一,因而准确获取土壤的盐渍化信息对于实时掌握其分布范围以及合理地开展盐渍化治理工作具有重要意义。选取渭干河--库车河流域绿洲典型盐渍地作为研究区,以Landsat-TM多光谱遥感影像为基础数据源,首先对影像进行最小噪声分离处理（MNF变换）并计算其像元纯度指数（Pixel Purity Index, PPI）,选取能表征区域特征信息的前三个波段构建MNF三维光谱特征空间,然后在向量空间和单行体理论的指导下,结合实地调查,提出“植被亮点区”概念,并定义“盐渍化距离指数（Soil Salinization Distance Index, SDI）”为多维向量空间中包含于单行体中的盐渍化像元到“植被亮点区”的归一化距离。最后利用不同盐分环境下的实测数据对SDI进行精度验证。结果显示：在低植被覆盖区,即中度、重度盐渍化区,SDI与0~10cm内土壤盐分含量相关性要高于0~20cm,R²＞0.83。在相对高覆盖区,即农田和轻度盐渍化区,SDI与0~20cm内平均盐分含量相关性要高于0~10cm,R²＞0.81。0~10cm层二者总体精度R²=0.81,0~20cm层二者相关性总体精度R²=0.72。研究表明,SDI指数模型简单、易于构建,精度较高,具有一定的应用价值,有利于干旱区区域大尺度盐渍化的定量分析和监测工作。*  相似文献   

Near-Infrared-Spectroscopy for Determination of Carbon and Nitrogen in Indian Soils     

J. Dinakaran  Ankita Bidalia  Amit Kumar  Mohammad Hanief  Archana Meena  K.S. Rao 《Communications in Soil Science and Plant Analysis》2016,47(12):1503-1516

The present study aims to evaluate the potential of near-infrared reflectance (NIR) spectroscopy to determine the carbon and nitrogen content in soils and also to assess the effectiveness of NIR spectroscopy to predict carbon and nitrogen content in freshly collected soil samples. Soil samples (n = 179) were collected from different locations in India. Soil carbon and nitrogen contents were successfully predicted (R² = 0.90 for carbon and R² = 0.85 for nitrogen) by NIR spectroscopy. The root mean square error (RMSE) and ratio performance deviation (RPD) for the validation of predicted equations for carbon and nitrogen were 0.83 and 2.83 and 0.01 and 6.98, respectively. The efficacy of NIR spectroscopy on the prediction of carbon and nitrogen content in Indian soils is highly reliable. Water content in soil samples could affect the NIR absorbance spectra and in turn affect the quantification of carbon and nitrogen.  相似文献   

Optimised use of data fusion and memory-based learning with an Austrian soil library for predictions with infrared data     

Bernard Ludwig  Isabel Greenberg  Michael Vohland  Kerstin Michel 《European Journal of Soil Science》2023,74(4):e13394

Infrared spectroscopy in the visible to near-infrared (vis–NIR) and mid-infrared (MIR) regions is a well-established approach for the prediction of soil properties. Different data fusion and training approaches exist, and the optimal procedures are yet undefined and may depend on the heterogeneity present in the set and on the considered scale. The objectives were to test the usefulness of partial least squares regressions (PLSRs) for soil organic carbon (SOC), total carbon (C_t), total nitrogen (N_t) and pH using vis–NIR and MIR spectroscopy for an independent validation after standard calibration (use of a general PLSR model) or using memory-based learning (MBL) with and without spiking for a national spectral database. Data fusion approaches were simple concatenation of spectra, outer product analysis (OPA) and model averaging. In total, 481 soils from an Austrian forest soil archive were measured in the vis–NIR and MIR regions, and regressions were calculated. Fivefold calibration-validation approaches were carried out with a region-related split of spectra to implement independent validations with n ranging from 47 to 99 soils in different folds. MIR predictions were generally superior over vis–NIR predictions. For all properties, optimal predictions were obtained with data fusion, with OPA and spectra concatenation outperforming model averaging. The greatest robustness of performance was found for OPA and MBL with spiking with R² ≥ 0.77 (N), 0.85 (SOC), 0.86 (pH) and 0.88 (C_t) in the validations of all folds. Overall, the results indicate that the combination of OPA for vis–NIR and MIR spectra with MBL and spiking has a high potential to accurately estimate properties when using large-scale soil spectral libraries as reference data. However, the reduction of cost-effectiveness using two spectrometers needs to be weighed against the potential increase in accuracy compared to a single MIR spectroscopy approach.  相似文献   

Analysis of Soil Chemical Properties of Sand-Based Turfgrass Rootzone using Fourier Transform–Infrared Spectroscopy     

《Communications in Soil Science and Plant Analysis》2012,43(21):2709-2721

To evaluate diffuse reflectance Fourier transform–infrared (DRIFT) in near-infrared (NIR) and mid-infrared (MIR) regions in conjunction with partial least square regression analysis for sand-based turfgrass soils, soil samples were collected from greens 6 to 9 years old, composed of two rootzone mixtures and from two establishment fertilization regimes, at different depths (surface to 7.6 cm in 12 layers). Mid-infrared and NIR spectroscopy resulted in similar calibration accuracy for total organic carbon, total nitrogen, cation exchange capacity, electric conductivity, and pH with R ² > 0.80. The modeling process for MIR spectrum was repeated on sample subsets, which were grouped from the original samples based on rootzone mixtures, putting green age, and depth to test the robustness of prediction models. Results of this study suggested that DRIFT-NIR and DRIFT-MIR could be used to predict these properties of sand-based turfgrass soils providing the soil samples are from similar depths.  相似文献   

Measurement of Phosphorus in Soils by Near Infrared Reflectance Spectroscopy: Effect of Reference Method on Calibration     

《Communications in Soil Science and Plant Analysis》2012,43(15-16):1965-1974

Abstract

The use of ultraviolet (UV), visible (VIS), near infrared reflectance (NIR), and midinfrared (MIR) spectroscopy techniques have been found to be successful in determining the concentration of several chemical properties in soils. The aim of this study was to evaluate the effect of two reference methods, namely Bray and Resins, on the VIS and NIR calibrations to predict phosphorus in soil samples. Two hundred (n=200) soil samples were taken in different years from different locations across Uruguay with different physical and chemical characteristics due to different soil types and management. Soil samples were analyzed by two reference methods (Bray and Resins) and scanned using an NIR spectrophotometer (NIRSystems 6500). Partial least square (PLS) calibration models between reference data and NIR data were developed using cross‐validation. The coefficient of determination in calibration (R²) and the root mean square of the cross validation (RMSECV) were 0.58 (RMSECV: 3.78 mg kg^?1) and 0.61 (RMSECV: 2.01 mg kg^?1) for phosphorus (P) analyzed by Bray and Resins methods, respectively, using the VIS and NIR regions. The R² and RMSECV for P using the NIR region were 0.50 (RMSECV: 3.78 mg kg^?1) and 0.58 (RMSECV: 2.01 mg kg^?1). This study suggested that differences in accuracy and prediction depend on the method of reference used to develop an NIR calibration for the measurement of P in soil.  相似文献   

Developing Pedotransfer Functions for Predicting FC and PWP     

Afshin Honarbakhsh  Mohammad Tahmoures  Yaser Ostovari  Somaye Noroozi 《Communications in Soil Science and Plant Analysis》2017,48(21):2485-2495

Using pedotransfer functions (PTF) is a useful way for field capacity (FC) and permanent wilting point (PWP) prediction. The aim of this study was to model PTF to estimate FC and PWP using regression tree (RT) and stepwise multiple linear regressions (SMLR). For this purpose, 165 and 45 soil samples from UNSODA and HYPRES datasets were used for development and validation of new PTFs, respectively. %Clay, geometric mean diameter (dg), and bulk density (BD) were selected as predictor variables due to the highest correlation and lowest multicollinearity. The results showed that clay percentage with W* = 0.89 and dg with W* = ?0.57 were the most effective variables to predict PWP and FC, respectively. The RT method had a better performance (R² = 0.80, ME = ?0.002 cm³cm^?3, RMSE = 0.05 cm³cm^?3 for FC and R² = 0.85, ME = 0.003 cm³cm^?3, RMSE = 0.03 cm³ cm^?3 for PWP) than SMLR in estimation of FC and PWP.  相似文献   

Prediction of Texture of Cooked White Rice by Near‐Infrared Reflectance Analysis of Whole‐Grain Milled Samples     

Jean‐Francois Meullenet  Andy Mauromoustakos  Teri Bellman Horner  Bradley P. Marks 《Cereal Chemistry》2002,79(1):52-57

  相似文献   

Evaluation of ground-level and space-borne sensor as tools in monitoring nitrogen nutrition status in immature and mature oil palm     

Amiratul Diyana Amirruddin  Daljit Singh Karam 《Journal of plant nutrition》2018,41(3):371-383

Monitoring nitrogen (N) in oil palm is crucial for the production sustainability. The objective of this study is to examine the capability of visible (Vis), near infrared (NIR) and a combination of Vis and NIR (Vis + NIR) spectral indices acquired from different sensors for predicting foliar N content of different palm age groups. The N treatments varied from 0 to 2 kg per palm, subjected according to immature, young mature and prime mature classes. The Vis + NIR indices from the ground level-sensor that is green + red + NIR (G + R + NIR) was the best index for predicting N for immature palms (R² = 0.91), while Vis indices blue + red (B + R) and Green Red Index from the space-borne sensor were significantly useful for N assessment of young and prime mature palms (R² = 0.70 and 0.50), respectively. The application of vegetation indices for monitoring N status of oil palm is beneficial to examine extensive plantation areas.  相似文献   

Empirical predictions of plant material C and N mineralization patterns from near infrared spectroscopy, stepwise chemical digestion and C/N ratios     

Sander Bruun  Bo Stenberg  Jon Gudmundsson  Lars S. Jensen  Jesper Luxhøi  Anders Pedersen 《Soil biology & biochemistry》2005,37(12):2283-2296

Prediction of carbon (C) and nitrogen (N) mineralization patterns of plant litter is desirable for both agronomic and environmental reasons. Near infrared reflectance (NIR) spectroscopy has recently been introduced in decomposition studies to characterize biochemical composition. The purpose of the current study was to use empirical techniques to predict C and N mineralization patterns of a wide range of plant materials incubated under controlled temperature and moisture conditions. We hypothesized that the richness of information in the NIR spectra would considerably improve predictions compared to traditional stepwise chemical digestion (SCD) or C/N ratios. Initially, we fitted a number of empirical functions to the observed C and N mineralization patterns. The best functions fitted with R²=0.990 and 0.949 to C and N, respectively. The fractions of C and N mineralized at different points in time were then either predicted directly with regression functions or indirectly by prediction of the parameters of the empirical functions fitted to incubation data. In both cases, partial least squares (PLS) regressions were used and predictions were validated by cross-validations. We found that the NIR spectra (best R²=0.925) were able to predict C mineralization patterns marginally better than the SCD fractions (best R²=0.911), but considerably better than the C/N ratios (best R²=0.851). In contrast, N mineralization was better predicted by SCD fractions (best R²=0.533) than the C/N ratio (best R²=0.497), which was better than NIR predictions (best R²=0.446). Although the predictions with the NIR spectra were only slightly better for C and worse for N mineralization compared to SCD fractions, NIR spectroscopy still holds advantages, as it is a much less laborious and cheaper analytical method. Furthermore, exploration of the applications of NIR spectroscopy in decomposition studies has only just begun, and offers new ways to gain insights into the decomposition process.  相似文献   

辽西‘富士’苹果CND法营养诊断研究   总被引：3，自引：1，他引：2  

范元广  李壮  厉恩茂  李敏  程存刚 《植物营养与肥料学报》2015,21(2):485-491

【目的】辽西地区‘富士’苹果的栽培面积和产量占辽宁省较大比例,但其施肥管理缺乏科学理论依据,本研究采用Compositional Nutrient Diagnosis(CND)法对辽西‘富士’果园的营养状况开展叶片分析研究,初步探明辽西地区‘富士’果园营养状况,指导果园施肥管理。【方法】以86个有代表性的‘富士’苹果园为研究试材,测定了果园产量,果实品质以及叶片中氮(N)、磷(P)、钾(K)、钙(Ca)、镁(Mg)、铁(Fe)、铜(Cu)、锰(Mn)、锌(Zn)元素的含量。采用CND法对高生产水平果园进行划分,并对低产水平果园进行营养诊断。采用CND法对高产果园产量划分标准临界值进行确定;对果实品质的划分标准进行确定:第一,对果实品质的6个指标进行归一化;第二,对6个指标进行主成分分析,得到6个指标的综合评价得分;第三,采用CND法对果实品质综合得分进行划分,得到果实品质的划分标准临界值;选择同时满足产量和品质划分标准临界值的果园,结合专家咨询法,确定高生产水平果园的划分标准。根据高生产水平果园的营养状况确定CND法标准参比值,并以此为基础进行叶营养诊断,得到辽西‘富士’果园的叶营养状况。针对叶营养状况进行土壤营养丰缺状况的分析,得到辽西‘富士’果园的营养状况的整体情况。【结果】根据CND法的叶分析可以得到:高生产水平果园的产量划分临界值为44.556t/hm2,品质评价综合得分的划分临界值为0.792,辽西地区满足此条件的果园有13个,占总体采样园的15.12%;不同营养元素CND法叶标准参比值为:V*N=2.776,V*P=0.212,V*K=1.884,V*Ca=2.042,V*Mg=0.814,V*Fe=-2.470,V*Cu=-5.090,V*Mn=-2.631,V*Zn=-3.867,V*R=6.330;SD*N=0.173,SD*P=0.144,SD*K=0.155,SD*Ca=0.266,SD*Mg=0.307,SD*Fe=0.189,SD*Cu=0.474,SD*Mn=0.467,SD*Zn=0.325,SD*R=0.134;辽西‘富士’苹果低生产水平果园需肥顺序为:FeMnPNCaKCuZnMg。【结论】CND法使叶分析研究可同时考虑果园产量和果实品质。以此方法进行分析,辽西地区高生产水平果园的产量划分临界值为38.451 t/hm2,品质评价综合得分的划分临界值为0.792,辽西地区满足此条件的果园有13个,仅占总体采样园的15.12%;辽西地区‘富士’苹果的树体营养状况表现为Fe、Mn、P和N元素缺乏;土壤丰缺状况表现为Fe、Mn元素含量适宜,N、P元素缺乏;建议辽西‘富士’低生产水平果园的N、P、K施用采用少量多次原则,适当增加Fe、Mn元素叶面肥的施用。  相似文献   

Can Near or Mid‐Infrared Diffuse Reflectance Spectroscopy Be Used to Determine Soil Carbon Pools?     

《Communications in Soil Science and Plant Analysis》2012,43(15-20):2307-2325

Abstract

The objective of this study was to compare mid‐infrared (MIR) an near‐infrared (NIR) spectroscopy (MIRS and NIRS, respectively) not only to measure soil carbon content, but also to measure key soil organic C (SOC) fractions and the δ¹³C in a highly diverse set of soils while also assessing the feasibility of establishing regional diffuse reflectance calibrations for these fractions. Two hundred and thirty‐seven soil samples were collected from 14 sites in 10 western states (CO, IA, MN, MO, MT, ND, NE, NM, OK, TX). Two subsets of these were examined for a variety of C measures by conventional assays and NIRS and MIRS. Biomass C and N, soil inorganic C (SIC), SOC, total C, identifiable plant material (IPM) (20× magnifying glass), the ratio of SOC to the silt+clay content, and total N were available for 185 samples. Mineral‐associated C fraction, δ¹³C of the mineral associated C, δ¹³C of SOC, percentage C in the mineral‐associated C fraction, particulate organic matter, and percentage C in the particulate organic matter were available for 114 samples. NIR spectra (64 co‐added scans) from 400 to 2498 nm (10‐nm resolution with data collected every 2 nm) were obtained using a rotating sample cup and an NIRSystems model 6500 scanning monochromator. MIR diffuse reflectance spectra from 4000 to 400 cm^?1 (2500 to 25,000 nm) were obtained on non‐KBr diluted samples using a custom‐made sample transport and a Digilab FTS‐60 Fourier transform spectrometer (4‐cm^?1 resolution with 64 co‐added scans). Partial least squares regression was used with a one‐out cross validation to develop calibrations for the various analytes using NIR and MIR spectra. Results demonstrated that accurate calibrations for a wide variety of soil C measures, including measures of δ¹³C, are feasible using MIR spectra. Similar efforts using NIR spectra indicated that although NIR spectrometers may be capable of scanning larger amounts of samples, the results are generally not as good as achieved using MIR spectra.  相似文献