基于高光谱特征参数的冬小麦氮营养指数估算

为了实现快速高精度获取冬小麦氮营养指数的高光谱监测技术，利用美国SVC HR-1024I型野外光谱辐射仪对2017-2019年关中地区的冬小麦进行遥感监测，获取“三边”参数、任意两波段光谱指数和植被指数，通过相关性分析和逐步回归分析方法筛选冬小麦氮营养指数的敏感光谱参数，结合偏最小二乘回归(PLSR)、随机森林算法(RFR)、支持向量机回归(SVR)和梯度增强回归(GBDT)建立冬小麦氮营养指数模型，并对模型估算精度进行验证。结果表明，从拔节期到灌浆期，各时期的氮营养指数与任意两波段光谱指数均呈极显著相关，其中拔节期氮营养指数与任意两波段光谱指数相关性均高于其他时期，且基于一阶导数光谱的归一化光谱指数和比值光谱指数与氮营养指数的相关系数最大，为0.66。拔节期基于梯度增强回归的冬小麦氮营养指数预测模型的决定系数(r²)和均方根误差(RMSE)分别为0.96和0.05,模型验证的r²、RMSE和相对预测偏差(RPD)分别为0.95、0.12和2.12,模型预测精度最高。因此，拔节期基于梯度增强回归的冬小麦氮营养指数估算模型可用于冬小麦氮营养监测...     

截形叶螨危害下枣叶片叶绿素含量高光谱估算模型

以不同截形叶螨(Tetranychus truncatus Ehara)危害等级下枣叶片高光谱和叶绿素含量数据为基础,分析不同截形叶螨危害等级(0级、1级、2级、3级、4级)下枣叶片高光谱特征,构建基于一阶微分光谱的不同截形叶螨危害等级枣叶片叶绿素含量高光谱线性回归估测模型。结果表明:截形叶螨危害造成叶片中叶绿素含量减少,导致光谱反射率降低,表现为随危害等级的增加叶绿素含量呈逐级减少趋势。在不同截形叶螨危害等级枣叶片叶绿素估测模型中,危害等级为0级时,模型拟合度最好,达到0.810,表明利用高光谱数据构建不同危害等级枣叶片叶绿素含量估算模型具有一定的潜力,对危害植被叶片的虫害诊断意义重大。     

基于高光谱技术的茶鲜叶含水率检测与分析

茶鲜叶含水率是茶叶加工业中衡量茶叶品质的一个重要指标。为了实现茶叶加工过程中茶鲜叶含水率的快速检测,本文提出了一种应用高光谱技术分析茶鲜叶含水率的无损检测方法。通过对茶鲜叶高光谱图像感兴趣区域光谱数据的提取,利用4种不同的算法对原始数据进行预处理,采用逐步回归分析法对预处理后的数据提取特征波长,并采用多元线性回归法、偏最小二乘回归建立特征波长和茶鲜叶含水率定量分析模型。研究结果表明,经过卷积平滑处理后的正交信号校正的预处理结合逐步回归分析法所建立的偏最小二乘回归茶鲜叶含水率预测效果最佳,模型校正集、交叉验证集和预测集的相关系数分别为0.8977、0.8342和0.7749,最小均方根误差分别为0.0091、0.0311和0.0371。由此可见,高光谱技术能有效的实现茶鲜叶含水率的检测,这为茶叶加工业中衡量茶叶品质提供了新的检测方法。     

实测高光谱和HSI影像的区域土壤含水量遥感监测研究

基于典型研究区植被冠层实测高光谱数据和HSI高光谱影像数据,通过相关分析选择与不同深度土壤含水量响应敏感波段,建立两者的土壤含水量反演模型,并用实测高光谱土壤含水量反演模型校正HSI影像土壤含水量反演的模型。结果表明:土壤含水量响应敏感波段区域为450~650 nm和850~920 nm;两种土壤含水量反演模型对土壤深度为0~10 cm的土壤含水量估算效果最好,其中实测冠层高光谱土壤含水量反演模型精度高于HSI影像土壤含水量反演模型,判定系数(R~2)分别为0.659和0.557;经过校正的HSI影像土壤含水量反演模型精度有了较大的提高,判定系数(R~2)从0.557提升到0.719,均方根误差(RMSE)为0.043 5,较好地提高了区域尺度条件下土壤含水量监测精度,因此运用该方法进行土壤含水量遥感监测是可行的,为进一步提高区域尺度下土壤含水量定量遥感监测提供参考借鉴。     

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.     

作物氮素营养诊断方法研究进展

改进氮素管理对农业生产和生态环境保护具有重要的意义。本文综述了几种氮素营养诊断方法的优缺点,重点介绍了主动遥感光谱仪G reenSeeker的特点,及其进行氮素诊断的机理、研究进展和其在国内的应用前景。     

Spatial modelling of the fraction of photosynthetically active radiation absorbed by a boreal mixedwood forest using a lidar–hyperspectral approach

The spatial variability of the fraction of photosynthetically active radiation absorbed by the canopy (fPAR) was characterized for a heterogeneous boreal mixedwood forest site located in northern Ontario, Canada, based on relationships found between fPAR and light detection and ranging (lidar) data over different canopy architectures. Estimates of fPAR were derived from radiation measurements made above the canopy at a flux tower and below-canopy radiation was measured across a range of species compositions and canopy architectures. Airborne lidar data were used to characterize spatial variability of canopy structure around the flux tower and a map of mean canopy chlorophyll concentration was derived from airborne hyperspectral imagery. Different volumes of lidar points for the locations directly above each photosynthetically active radiation (PAR) sensor were examined to determine if there is an optimal method of relating lidar returns to estimated fPAR values.The strongest correlations between mean lidar height and fPAR occurred when using points that fell within a theoretical cone which originated at the PAR sensor having a solid angle α = 55°. For diffuse conditions, the correlation (r) between mean lidar height versus fPAR × chlorophyll was stronger than between mean lidar height versus fPAR by 8% for mean daily fPAR and from 10 to 20% for diurnal fPAR, depending on solar zenith angle. For direct light conditions, the relationship was improved by 12% for mean daily fPAR and 12–41% for diurnal relationships.Linear regression models of mean daily fPAR × chlorophyll versus mean lidar height were used in conjunction with gridded lidar data and the canopy chlorophyll map to generate maps of mean daily fPAR for direct and diffuse sunlight conditions. Site average fPAR calculated from these maps was 0.79 for direct light conditions and 0.78 for diffuse conditions. When compared to point estimates of mean daily fPAR calculated on the tower, the average fPAR was significantly lower than the point estimate. Subtracting the direct sunlight fPAR map from the diffuse sunlight fPAR map revealed a distinct spatial pattern showing that areas with open canopies and relatively low chlorophyll (e.g., black spruce patches) have a higher fPAR under direct sunlight conditions, while closed canopies with higher chlorophyll (e.g., deciduous species) absorb more PAR under diffuse conditions. These findings have implications for scaling from point measurements at flux towers to larger resolution satellite imagery and addressing local scale heterogeneity in flux tower footprints.     

油桃外部缺陷的高光谱成像检测

采用高光谱(420~1 000 nm)成像技术对"中油9号"油桃的4种外部缺陷(裂纹果、锈病果、异形果和暗伤果)进行检测判别。对400个样本(4种外部缺陷样本和完好样本)运用偏最小二乘回归(PLSR)从全波段中分别提取了10条特征波长,分别为497、534、657、677、696、709、745、823、868、943 nm。缺陷样本的高光谱图像经过主成分分析后,对876 nm下的单波段图像通过掩膜、Sobel算子处理,并对主成分图像经过区域生长算法实现缺陷样本的缺陷区域分割。对光谱数据进行主成分分析得到前10个主成分值,并对图像数据采用灰度共生矩阵(GLCM)提取得到6项图像纹理指标(均值、对比度、相关性、能量、同质性、熵值)。将主成分值和纹理值融合建立极限学习机(ELM)模型对油桃外部缺陷进行检测判别。结果表明,该模型对缺陷样本的判别正确率为91.67%,完好样本的正确率为100%。     

基于平稳小波变换的冬小麦覆盖度高光谱监测

在2010与2011年度冬小麦生长季,通过大田小区试验,实测了冬小麦冠层的高光谱反射率与覆盖度。分析了不同覆盖度下的冬小麦冠层光谱特征以及不同生育期冬小麦冠层光谱反射率与覆盖度的相关性,建立了基于归一化植被指数(NDVI)与比值植被指数(RVI)、小波能量系数的不同生育期冬小麦覆盖度估算模型。结果表明:覆盖度越大,冬小麦光谱反射率在可见光波段越小,在近红外波段越大。在可见光波段,光谱反射率与覆盖度负相关,在"红边"处,由负相关变成正相关。在返青期、拔节期,NDVI估算效果好(R2为0.835 9、0.805 7);在抽穗期、灌浆期,RVI估算效果好(R2为0.803 1、0.829 4)。在返青期、拔节期、抽穗期、灌浆期,以高频、低频小波能量系数为自变量的冬小麦覆盖度估算模型的R2分别达到0.911 2、0.895 4、0.880 2、0.927 5。