应用光谱反射测定的植物、土壤及混合光谱特性

以野外采集的土壤与活体植物叶片为研究对象,采用美国生产的SVC HR-1024便携式地物光谱仪,测定了纯土壤、纯植物叶片以及不同植物叶片盖度下的土壤光谱反射率,并对混合光谱特性进行了分析研究,建立了植物叶片盖度光谱估算模型。结果表明:(1)随着植物叶片盖度的增加,土壤光谱曲线逐渐呈现"五谷四峰"的特征,但始终不会高于植被光谱曲线;(2)植物叶片盖度与原始高光谱组成的差值、比值和归一化值3种形式的指数存在着良好的相关性,差值指数的显著性最为明显,组合较好的波段为410~710 nm与700~1400 nm,能反映相关系数最大的波段为690 nm和450 nm;(3)通过一元线性回归法、多元线性逐步回归法和偏最小二乘回归法预测植物叶片盖度可知:光谱特征参数与植物叶片盖度之间具有相关性,其中光化学指数与植被衰减指数的相关系数较大,模型拟合度较高,R^2均达到0.8以上;3种光谱特征参数(植被指数、红边参数、光谱吸收特征参数)中,植被指数与植物叶片盖度建立的模型最稳定、精度最高,可以有效地估算植物叶片盖度3种方法中,多元线性逐步回归法最适宜建立植物叶片盖度预测模型。     

模极大值法在食醋近红外光谱定量分析中的应用

以镇江香醋中还原糖定量分析为例，研究小波分析中模极大值法在建立预测模型方面的应用，对基于模极大值处理光谱信息前后的预测模型进行比较发现，该方法可以有效地消除光谱的平移误差，且具备一定的分峰效果和提取光谱信息的功能，因此预测模型的预测精度和稳定性都有较大的提高，在光谱定量分析中有广泛的应用前景。     

车载式土壤光-电特性参数采集系统研究

土壤电导率是衡量土壤传导电流能力的一种固然属性,除了能够反映土壤质地外,还能够反映土壤的含水率、含盐量、有机物含量等特性。利用土壤光谱数据可以分析出土壤含水率、养分等,且测量过程中无需采样、搅动土壤。通过土壤电导率数据和光谱数据的综合、校正,能够提高系统的精度。基于嵌入式技术开发了土壤电导率和光谱反射率综合检测系统。土壤电导率测量系统采用基于改进的电流-电压四端法原理,使用深松犁的尖端作为电极传感器,能够在测量的同时完成松土的作用。光谱测量系统使用微型光谱传感器采集光谱数据,并进行实时处理。在采集土壤电导率、光谱数据的同时,系统同步采集GPS信息,并和土壤电导率、光谱数据一起保存,供进一步绘制土壤特性分布图使用。系统能够实时综合处理多种数据,并进行显示、保存等操作,具有良好的应用 前景。     

小麦叶片氮含量与冠层反射光谱指数的定量关系

本文以3种蛋白质类型的小麦品种在不同施氮水平下的3年田间试验为基础，研究了小麦叶片氮含量与冠层反射光谱的定量关系。结果显示，不同试验中拔节后叶片氮含量均随施氮水平呈上升趋势，同时冠层光谱反射率在不同施氮水平下存在明显差异。对于低、中、高籽粒蛋白质含量的品种，叶片氮含量与冠层反射光谱的归一化植被指数NDVI (1 220, 710)和红边位置均有密切的定量关系，决定系数在0.80左右。对于不同品质类型小麦品种，均可利用统一的回归方程描述其叶片氮含量随反射光谱参数的变化，对于低蛋白类型品种，采用单独的回归系数即可提高叶片氮含量估测的准确性。本研究确立的小麦叶片氮含量与冠层反射光谱的定量关系可用于不同的小麦品种、生育时期和施氮水平，为小麦氮素营养的监测诊断与精确施肥等提供理论依据和技术途径。     

冬小麦不同株型品种光谱响应及株型识别方法研究

以直立和平展2种株型的冬小麦品种为材料，研究了它们的光谱响应以及田间植被覆盖度的差异，探讨了利用冠层光谱反射率、光谱特征参量NDVI及植被覆盖度识别小麦株型的方法。结果表明，（1）小麦不同株型品种在近红外波段（700～1300 nm）光谱反射率有明显差异，生育前期平展型品种高于直立型品种，并以拔节期的差异为最显著，随着生育进程差异逐渐变小。拔节期是进行株型识别的最佳时期，并且此期冠层的敏感波段680 nm和760～900 nm的反射率在2种株型品种之间差异明显。（2）小麦冠层叶面积指数（LAI）与归一化差异植被指数NDVI(680,890)呈正相关，并且不同生育阶段其相关程度有差异，这是利用NDVI和植被覆盖度(COV)识别不同株型的基础。（3）相同COV条件下，直立型品种的NDVI高于平展型品种的NDVI，并且随着COV的增加，差异逐渐变小，二者的变化关系体现了直立型品种株型紧凑和平展型品种株型披散的特点，利用NDVI和COV的关系可以对株型进行识别，以小麦拔节期为最佳识别阶段，此期2种株型品种的NDVI具有显著差异（P＜0.05）。     

基于近红外光谱分析的土壤全氮含量估测研究

应用近红外光谱分析技术对比研究基于土壤风干样本和鲜样来预测全氮含量的可行性。选取水稻土为研究对象,首先分析了不同水分土壤的光谱特征,显示随水分含量增加,吸光度升高,且鲜样的吸光度高于干样。通过比较不同预处理方法,对土壤干鲜样分别采用逐步多元回归(SMLR)和偏最小二乘法(PLSR)建立了相应的近红外模型。结果表明,利用近红外光谱均可预测干鲜土壤样本的全氮含量,特别是利用偏最小二乘法建立的标定模型,预测精度高,反演性较好,鲜样和干样外部验证决定系数分别达到0.89和0.91,相对误差仅为6.92%和5.92%,研究结果可以为田间土壤全氮含量的估测提供技术依据和参考。     

Rainfall and tillage effects on soil structure after alfalfa conversion to maize on a clay loam soil in New York

Soil degradation is accelerated when perennial crops are converted to annual row crops, primarily due to increased soil disturbance from tillage. Subsequent heavy rainfall may induce soil settling, reduce macroporosity and increase hardsetting upon drying. An experiment involving plow and no-tillage and two simulated rainfall treatments (‘wet’ and ‘dry’) was conducted on Kingsbury clay loam soil in northern New York in 1992 and 1993 to study their effects on soil structure under maize (Zea mays L.) after conversion from alfalfa (Medicago sativa L.), and to evaluate the use of spectral analysis of micropenetrometer observations for studying soil aggregation. Undisturbed soil cores were collected from the row and trafficked and non-trafficked interrow positions at the 0.05 and 0.15 m depths and used for laboratory measurement of soil strength and pore system properties. These well-structured soils show a high contribution (up to 0.15 m³ m⁻³) of macropores to the total porosity of the soil. Soil strength was generally slightly higher for no-till (NT) than plow till (PT), although only significant in 1992. Soil strength in the surface layer did not change significantly with drying. Spectral density patterns did not show strong treatment effects, although distinct peaks reflect 3.0–3.5 mm stable structural units within macroaggregates. Simulated rainfall treatments and tillage treatments generally did not strongly affect measured soil properties, presumably due to stable soil structure. Structurally stable clay loam soils show little effect of tillage or settling on soil physical properties in the first years after alfalfa to maize conversion, and have good potential for long-term annual crop production if properly managed.     

浙江省石灰土光谱特征及其自动识别分类技术研究

本文通过石灰土专题调查，室内土壤光谱测定及其特定及其特征分析，并在此基础上进行土壤光谱自动识别分类方法的对比试验。明确了浙江省石灰土的四个土类，六个土属的光谱特征分异规律；提出了一种参数少，运算量小，精度高的光谱自动识别新方法－分层提取判别法。     

以光谱反射曲线评估染色单板的耐光性

以杨木和桦木单板为试材,引入光谱反射曲线研究了室内自然光环境下染色单板的光变色过程,分析了树种、漂白处理、染料种类及浓度对染色单板耐光性的影响。结果表明,光辐射后染色单板K/S曲线呈下降趋势,光谱反射率曲线向黄光方向移动,染色单板发生黄化。漂白处理加速了染色单板的光变色。染料品种对染色单板耐光性影响显著,酸性蓝染色单板的光谱反射率曲线变化明显,而酸性大红的变化不大。利用光谱反射曲线可快速直观地评估染色单板耐光性的优劣。     

Hyper-spectral estimation of wheat biomass after alleviating of soil effects on spectra by non-negative matrix factorization

Hyper-spectral technology has been proven to be an effective method for the fast and non-destructive monitoring of crop biomass. However, the biomass estimation accuracy of this method is limited due to the effects of background factors, such as soils and water. In this study, a spectral separation method, non-negative matrix factorization (NMF), was proposed to alleviate the effects of soil on spectra. With the application of the NMF method, pure vegetation spectra were extracted from the field-observed spectra of wheat canopy, which were collected in four growing seasons from the tillering to the booting stages of wheat. Then, prediction models of wheat biomass (WB) were established and validated using the extracted spectra with the partial least squares regression (PLSR) method. The results showed that the NMF method could effectively separate the vegetation spectra from the mixed canopy spectra. Based on the extracted vegetation spectra, the WB prediction accuracy could be greatly improved with an increase of 31.7% for the R²_p and an increase of 46.6% for the ratio of performance to deviation (RPD) as compared to the original spectra, indicating that the NMF method could significantly improve the performance of the WB prediction model. This method has potential application in the estimation of biomass using remote sensing technology.