农牧交错区青贮玉米粗蛋白含量分析及高光谱反演

为了探讨青贮玉米全株粗蛋白含量的基因型差异和生育进程中的变化特点,对农牧交错区引进和选育的24份青贮玉米杂交种的粗蛋白含量进行了测定分析.结果表明,不同基因型全株粗蛋白含量具有较大的差异,变幅为6.24%～10.01%,平均值为7.77%;全株粗蛋白含量和生物产量之间总体没有明显的相关关系,分组分析可以呈现出正相关、负相关和无相关三种关系.叶片和全株粗蛋白含量有随生育期的推移而下降的趋势,而且叶片对粗蛋白含量的贡献在前期最高,在中期最低;茎部对粗蛋白含量的贡献在中期最高,在后期最低.各生育期的叶片和全株粗蛋白含量之间具有显著或极显著正相关关系;生育前期叶片粗蛋白含量和收获期全株粗蛋白含量之间具有极显著正相关关系,生育前期和中期全株粗蛋白含量与生育后期全株粗蛋白含量之间具有极显著正相关关系.选用植株冠层12个波长的高光谱反射率的一阶导数,与叶片和全株粗蛋白含量作回归分析,可以获得很好的效果,拟合度(R2)可以达到0.8以上。     

Physiological interpretation of a hyperspectral time series in a citrus orchard

Hyperspectral remote sensing for monitoring horticultural production systems requires the understanding of how plant physiology, canopy structure, management and solar elevation affect the retrieved canopy reflectance during different stages of the phenological cycle. Hence, the objective of this study was to set up and to interpret a hyperspectral time series for a mature and healthy citrus orchard in the Western Cape province of South Africa considering these effects. Based on the remotely sensed data, biophysical parameters at the canopy level were derived and related to known observed physiological and phenological changes at the leaf level and to orchard management. Fractions of mature fruit, flowers, and sunburnt leaves were considered, and indices related to canopy structure chlorophyll content and canopy water status were calculated.Results revealed small cover fractions of mature fruit, flowers and sunburnt leaves of respectively 2.1%, 3.1% and 7.0%, but the high spectral contrast between flowers and leaves allowed a successful classification of flowering intensity. Furthermore, it was shown that canopy level time series of vegetation indices were sensitive to changes in solar elevation and soil reflectance which could be reduced by applying an empirical soil line correction for the most affected indices. Most trends in vegetation indices at the canopy level could be explained by a combination of changes at the leaf level (chlorophyll, carotenoids, dry matter), changes in canopy structure (leaf area index and leaf angle distribution) and changes in cover fractions of vegetative flushes, flowers and sunburnt leaves. The transformed chlorophyll absorption ratio index over the optimised soil adjusted vegetation index (MCARI/OSAVI) was best related to leaf level trends in chlorophyll content. Seasonal changes in the photochemical reflectance index (PRI) were linked to inverse changes in the carotenoid-to-chlorophyll ratio. Canopy structure indices (the modified triangular vegetation index or MTVI2 and the standardized leaf area index determining index or sLAIDI) were sensitive to changes in leaf area index, average leaf angle as well to management interactions (pruning and harvest). Canopy water status was highly impacted during the spring flush due to expanding leaves that concealed trends in the underlying mature leaves. Seasonal trends in soil and weeds reflectance were related to changes in volumetric soil water content and to the earlier and reduced growth period of non-irrigated weeds.     

Seasonal variation in canopy reflectance and its application to determine the water status and water use by citrus trees in the Western Cape, South Africa

This study describes the diurnal and seasonal dynamics of the canopy reflectance, water use and water status of Midknight Valencia citrus trees under semi-arid conditions. Hyperspectral canopy reflectance data was collected on 30 trees at monthly intervals over a period of 16 months in a commercial orchard in South Africa. The mean canopy reflectance in the wavelength range 350-2500 nm followed a clear seasonal trend influenced by environmental conditions and tree phenology. Mean monthly reflectance peaked in summer (∼22%) while the lowest value (∼15%) was reached in winter with the seasonal changes in the sun's position accounting for a significant proportion of the variations. A sensitivity analysis of a Penman-Monteith transpiration model showed that water use by individual trees changed by up to 13% when the canopy reflectance was varied over the seasonal range of measured values. This suggested that the seasonal changes in tree water use influenced the seasonal trend of the canopy reflectance. Thus monitoring the canopy reflectance of citrus trees could offer information on the tree water status. To test this, sap flow data of water uptake and loss by the trees were compared with the canopy spectra. Sap flow data showed a heavy reliance by the citrus trees on the internally stored water with up to 25% of the daily total transpiration withdrawn from the trees’ internal water storage pools when soil water was limited. This depletion of internally stored water, and hence the change in tree water status, was detected using spectral indices based on the first order derivatives of the canopy reflectance centered at two and, at most, four spectral bands. We conclude that even if citrus trees are evergreen, their canopy reflectance changes significantly throughout the year with a considerable impact on tree energy balance and water use. In addition, the contribution of the internally stored water to daily transpiration is a possible indicator of drought stress for citrus trees detectable from changes in canopy reflectance and it has potential applications in irrigation scheduling using canopy level spectral information.     

基于光谱分析的土壤重金属含量估算研究——以三江源区玉树县和玛多县为例

以三江源区玉树县和玛多县为研究区,利用实验室测定的As、Cu、Pb、Zn、Cr、Cd、Hg元素含量和室内采集的土壤原始光谱及其4种转换形式,建立了光谱指标与重金属含量的多元回归模型,利用决定系数(R2)、相对分析误差(RPD)及均方根误差(RMSE)评价模型的精度。研究结果表明,土壤As、Cu、Pb、Zn、Cr、Cd含量与SOM、Fe、Mn、Al、Mg等元素具有显著相关关系,Hg元素则未达到显著性水平。As、Cu、Pb、Zn、Cr和Cd元素估算模型回归方程R2达到了0.5以上,均通过了显著性检验,其中Pb、Zn和Cr元素验证样本RPD均达到了1.4以上,模型具备粗略估算能力;As、Cu和Cd元素验证样本RPD均低于1.4,模型不具备粗略估算能力。Hg元素估算模型回归方程的R2为0.28,未能通过显著性检验,无法用于对Hg含量的估算。     

Soil heterogeneity at the field scale: a challenge for precision crop protection

Crop protection seldom takes into account soil heterogeneity at the field scale. Yet, variable site characteristics affect the incidence of pests as well as the efficacy and fate of pesticides in soil. This article reviews crucial starting points for incorporating soil information into precision crop protection (PCP). At present, the lack of adequate field maps is a major drawback. Conventional soil analyses are too expensive to capture soil heterogeneity at the field scale with the required spatial resolution. Therefore, we discuss alternative procedures exemplified by our own results concerning (i) minimally and non-invasive sensor techniques for the estimation of soil properties, (ii) the evidence of soil heterogeneity with respect to PCP, and (iii) current possibilities for incorporation of high resolution soil information into crop protection decisions. Soil organic carbon (SOC) and soil texture are extremely interesting for PCP. Their determination with minimally invasive techniques requires the sampling of soils, because the sensors must be used in the laboratory. However, this technique delivers precise information at low cost. We accurately determined SOC in the near-infrared. In the mid-infrared, texture and lime content were also exactly quantified. Non-invasive sensors require less effort. The airborne HyMap sensor was suitable for the detection of variability in SOC at high resolution, thus promising further progress regarding SOC data acquisition from bare soil. The apparent electrical conductivity as measured by an EM38 sensor was shown to be a suitable proxy for soil texture and layering. A survey of arable fields near Bonn (Germany) revealed widespread within-field heterogeneity of texture-related ECa, SOC and other characteristics. Maps of herbicide sorption and application rate were derived from sensor data, showing that optimal herbicide dosage is strongly governed by soil variability. A phytoassay with isoproturon confirmed the reliability of spatially varied herbicide application rates. Mapping areas with an enhanced leaching risk within fields allows them to be kept free of pesticides with related regulatory restrictions. We conclude that the use of information on soil heterogeneity within the concept of PCP is beneficial, both economically and ecologically.     

玉米叶片高光谱特征及与叶绿素、类胡萝卜素相关性的研究

测定了不同品种类型、不同株型、不同发育期的春玉米叶片及其它器官、不同叶位叶片及叶片不同部位的高光谱反射率和叶片的叶绿素、类胡萝卜素含量。结果表明,玉米不同鲜器官的反射率随波长的变化趋势相似,但数值存在较大差异;不同叶位的叶片、不同品种同一叶位的叶片、叶片的不同部位的光谱反射率有差异;叶片光谱反射率随含水率减少而升高,它们的变化趋势相似;叶片叶绿素和类胡萝卜素浓度与光谱植被指数R800/R550、R673/R640、PSSRa、PSNDa、RCh、CARI、λred、Dλred和Sred具有极显著相关。说明可以通过光谱方法来监测玉米长势和估算叶绿素、类胡萝卜素含量。     

Multi-source land cover classification for forest fire management based on imaging spectrometry and LiDAR data

Forest fire management practices are highly dependent on the proper monitoring of the spatial distribution of the natural and man-made fuel complexes at landscape level. Spatial patterns of fuel types as well as the three-dimensional structure and state of the vegetation are essential for the assessment and prediction of forest fire risk and fire behaviour. A combination of the two remote sensing systems, imaging spectrometry and light detection and ranging (LiDAR), is well suited to map fuel types and properties, especially within the complex wildland–urban interface. LiDAR observations sample the spatial information dimension providing explicit geometric information about the structure of the Earth's surface and super-imposed objects. Imaging spectrometry on the other hand samples the spectral dimension, which is sensitive for discrimination of surface types. As a non-parametric classifier support vector machines (SVM) are particularly well adapted to classify data of high dimensionality and from multiple sources as proposed in this work. The presented approach achieves an improved land cover mapping adapted to forest fire management needs. The map is based on a single SVM classifier combining the spectral and spatial information dimensions provided by imaging spectrometry and LiDAR.     

高光谱遥感在荒漠化监测中的应用

论述并建立了适合于高光谱遥感技术的荒漠化监测指标,提出了基于高光谱分辨率数据处理算法的荒漠化监测评价指标信息的提取方法。初步构建了ＴＭ、高光谱分辨率成像光谱仪和地面调查相结合的荒漠化监测的技术框架。     

基于反射率及导数的草原植被冠层光合有效吸收分量高光谱反演

利用ASD便携式野外光谱仪和光量子仪实测了6种草原植被类型关键生育期的反射光谱数据和光合有效辐射值,利用可见光波段处反射率及近红外波段区处一阶导数分别与fPAR建立逐步回归方程,同时,将各波段反射率与各波段导数光谱建立逐步回归方程。结果表明:典型草原光合有效辐射分量与可见光反射率相关性好于近红外波段反射率,其中在405和470 nm波段相关性最好;fPAR与一阶导数相关关系在855和965 nm波段处较强。fPAR与405和470 nm反射率以及965 nm一阶导数的多波段逐步回归分析结果取得了较单波段和NDVI最优的估算效果,R²达0.939。利用高光谱数据进行fPAR估算时,需要综合考虑可见光和近红外波段信息,同时也要充分考虑反射率与反射率导数的方法。水分强吸收的光谱波段具有提高fPAR估算精度的潜力。     

基于高光谱图像技术的小麦种子分类识别研究

为了探讨高光谱图像技术在小麦种子分类识别中应用的可行性,采集了河南地区主要种植的7个小麦品种的种子高光谱图像及900～1 700nm范围的光谱信息,建立了主成分分析法(PCA)-支持向量机(SVM)分类模型。运用PCA对光谱数据进行降维处理,结合SVM模型比较了不同实验条件下小麦种子分类准确率以及在最佳条件下3个、4个和6个品种种子的分类准确率。结果显示,3个品种间种子分类准确率除个别外平均达到95%以上,4个品种间种子分类准确率在80%左右,6个品种间种子分类准确率在66%左右。这说明充分利用光谱信息可以对3个或4个小麦品种进行多籽粒分类。