近红外光谱技术在天然草地牧草营养价值评价中的应用及展望

近红外光谱技术(NIRS)是近几十年迅速发展的测试分析技术，由于准确、高效、无损等检测优势，在牧草营养价值评价领域得到广泛应用，但是在天然草地牧草方面应用较少。快速、实时评价天然草地牧草营养价值，为研究天然草地营养供给和营养载畜量提供基础数据，对草地畜牧业生产具有重要意义。文章阐述了近红外光谱技术的基本原理和特点，介绍了直接法和间接法评价牧草营养价值，分别从常规营养成分、矿物元素、抗营养成分、营养物质消化率4个层次综述近红外光谱技术在2个方法中的应用，并做出展望，以期建立基于NIRS技术的天然草地牧草营养价值数据库，为天然草地的科学管理和合理利用发挥重要作用。

     

The application of proximal visible and near-infrared spectroscopy to estimate soil organic matter on the Triffa Plain of Morocco

Soil organic matter (SOM) is a fundamental soil constituent. The estimation of this parameter in the laboratory using the classical method is complex time-consuming and requires the use of chemical reagents. The objectives of this study were to assess the accuracy of two laboratory measurement setups of the VIS-NIR spectroscopy in estimating SOM content and determine the important spectral bands in the SOM estimation model. A total of 115 soil samples were collected from the non-root zone (0–20 cm) of soil in the study area of the Triffa Plain and then analysed for SOM in the laboratory by the Walkley–Black method. The reflectance spectra of soil samples were measured by two protocols, Contact Probe (CP) and Pistol Grip (PG)) of the ASD spectroradiometer (350–2500 nm) in the laboratory. Partial least squares regression (PLSR) was used to develop the prediction models. The results of coefficient of determination (R²) and the root mean square error (RMSE) showed that the pistol grip offers reasonable accuracy with an R² = 0.93 and RMSE = 0.13 compared to the contact probe protocol with an R² = 0.85 and RMSE = 0.19. The near-Infrared range were more accurate than those in the visible range for predicting SOM using the both setups (CP and PG). The significant wavelengths contributing to the prediction of SOM for (PG) setup were at: 424, 597, 1432, 1484, 1830,1920, 2200, 2357 and 2430 nm, while were at 433, 587, 1380, 1431, 1929, 2200 and 2345 nm for (CP) setup.     

Continuous Monitoring of Visible and Near-Infrared Band Reflectance from a Rice Paddy for Determining Nitrogen Uptake Using Digital Cameras

Abstract

A two-band digital imaging system —one band for the visible red band (RED, 630?670 nm) and the other for the near infrared band (NIR, 820?900 nm)— was devised and positioned at a height of 12 m above a rice field of 300 m² in area during the 2007 growing season. The imaging system automatically logged bird’seye view images at 10-min intervals from 0800?1600 every day. Radiometric corrections for the pairs of two-band images were done using solar irradiance sensors and preceding calibrations to calculate daily band-reflectance and the normalized difference vegetation index (NDVI) values for 9 plots of rice plants, with 3 levels of planting density and basal fertilization. The daily- averaged reflectance values in the RED and the NIR bands showed different but smooth seasonal changing patterns according to the growth of plants. At the maximum tiller number and the panicle formation stages, the RED and NIR reflectance values had correlation coefficients (r) of 0.79 and 0.81 with above-ground nitrogen absorption per unit land area (NA, g m-²), respectively, whereas the NDVI using the two band reflectance values showed r-value of -0.13. An empirically derived equation for the NA using two band reflectance values showed r-value of 0.96 and a root mean square of error (RMSE) 0.5 g m^–2 (10% of the mean observed NA) in the estimation for the original (not validated) data set acquired at the maximum tiller number and the panicle formation stages. The results indicated that reflectance observation in the RED and NIR bands using the digital imaging system was potentially effective for assessing rice growth.     

Hyper-spectral remote sensing to monitor vegetation stress

Background, aim, and scope  Vegetation stress diagnoses based on plant sampling and physiochemical analysis using traditional methods are commonly time-consuming, destructive and expensive. The measurement of field spectral reflectance is one basis of airborne or spaceborne remote sensing monitoring. Materials and methods  In this study, paddy plants were grown in the barrels evenly filled with 10.0 kg soil that was mixed respectively with 0, 2.5 × 207.2 and 5.0 × 207.2 mg Pb per 1,000 g soil. Rice canopy spectra were gathered by mobile hyper-spectral radiometer (ASD FieldSpec Pro FR, USA). Meanwhile, canopy leaves in the field-of-view (FOV) of spectroradiometer were collected and then prepared in the laboratory, (1) for chlorophyll measurement by Model 721 spectrophotometer, and (2) for Pb determination by atomic absorption spectrophotometer (SpectraAA-220FS). Results and discussion  Canopy spectral reflectance in the region of visible-to-near-infrared light (VNIR) increased, because ascended Pb concentration caused the decrease of canopy chlorophyll content. In the agro-ecosystem, however, heavy metal contamination is presented typically as mixture and their interactions strongly affect actually occurring effects. Normalized spectral absorption depth (D _n), and shifting distance (DS) of red edge position (REPs) revealed the differences in Pb concentration for canopy leaves, especially at the early tillering stage. Due to insufficient biomass of rice plants, the 30th day was not reliable enough for the selection of crucial growth stages. Some special sensitive bands might be omitted at the same time because of limited sample sets. Conclusions  Our initial experiments are still too few in the amounts of both metals and plants neither to build accurate prediction models nor to discuss the transformation from ground to air/spaceborne remote sensing. However, we are pleased to communicate that ground remote sensing measurements would provide reliable information for the estimation of Pb concentration in rice plants at the early tillering stage when proper features (such as DS and D _n) of reflectance spectra are applied. Recommendations and perspectives  Hyper-spectral remote sensing is a potential and promising technology for monitoring environmental stresses on agricultural vegetation. Further ground remote sensing experiments are necessary to evaluate the possibility of hyper-spectral reflectance spectroscopy in monitoring different kinds of metals’ stress on various plants.     

饲料中肉骨粉含量的近红外反射光谱检测方法

收集28份鸡饲料,31份猪饲料,25份牛饲料和肉骨粉7份,在饲料中掺入不同比例(0.5%～6.0%)的肉骨粉,制成分析样本.采用偏最小二乘(PLS)法,分别对光谱进行散射校正、平滑、一阶导数和二阶导数预处理,建立了鸡饲料、猪饲料和牛饲料中肉骨粉含量的近红外定量分析模型.利用验证集样本对定标模型进行了检验,鸡饲料、猪饲料和牛饲料中肉骨粉含量的真值与预测值之间的决定系数分别为0.9694、0.9846和0.9788;标准差分别为0.279、0.252和0.287;相对分析误差分别为5.663、6.865和5.889.结果表明,利用近红外光谱法测定饲料中的肉骨粉含量具有较高的预测精度.     

新型酸性亮蓝染色剂对蚕丝的染色匀度试验(英文)

新型酸性亮蓝是目前纺织印染的流行染色剂。用不均匀性指数对染色匀度进行分级,研究了该染色剂不同色度对蚕丝染色匀度的影响。以该染色剂的14个色度对蚕丝进行染色,测定各色度染液处理蚕丝的反光值(用K/S值表示),结果发现该染色剂对蚕丝染色的不均匀性指数在所有色度水平下并不是恒定的,染色均匀性在染色剂色度为1.5%时开始出现,其后随着色度增加染色匀度也越来越好,到染色剂色度为4%时染色匀度最好,之后染色匀度又变差。     

Spectroscopic detection of forest diseases:a review (1970-2020)

Sustainable forest management is essential to confront the detrimental impacts of diseases on forest eco-systems.This review highlights the potential of vegetation spectroscopy in improving the feasibility of assessing forest disturbances induced by diseases in a timely and cost-effec-tive manner.The basic concepts of vegetation spectroscopy and its application in phytopathology are first outlined then the literature on the topic is discussed.Using several opti-cal sensors from leaf to landscape-level,a number of for-est diseases characterized by variable pathogenic processes have been detected,identified and quantified in many coun-try sites worldwide.Overall,these reviewed studies have pointed out the green and red regions of the visible spec-trum,the red-edge and the early near-infrared as the spectral regions most sensitive to the disease development as they are mostly related to chlorophyll changes and symptom develop-ment.Late disease conditions particularly affect the short-wave-infrared region,mostly related to water content.This review also highlights some major issues to be addressed such as the need to explore other major forest diseases and geographic areas,to further develop hyperspectral sensors for early detection and discrimination of forest disturbances,to improve devices for remote sensing,to implement long-term monitoring,and to advance algorithms for exploitation of spectral data.Achieving of these goals will enhance the capability of vegetation spectroscopy in early detection of forest stress and in managing forest diseases.     

Assessing the effects of the clumping phenomenon on BRDF of a maize crop based on 3D numerical scenes using DART model

Inverting radiative transfer (R-T) models against remote sensing observations to retrieve key biogeophysical parameters such as leaf area index (LAI) is a common approach. Even if new inversion techniques allow the use of three-dimensional (3D) models for that purpose, one-dimensional (1D) models are still widely used because of their ease of implementation and computational efficiency. Nevertheless, they assume a random distribution of foliage elements whereas most canopies show a clumped organization. Due to that crude simplification in the representation of the canopy structure, sizeable discrepancies can occur between 1D simulations and real canopy reflectance, which may further lead to false LAI values. The present investigation aims to appraise to which extent the incorporation of a clumping index (noted λ) into 1D R-T model could improve the simulations of Bidirectional Reflectance Distribution Function (BRDF). Canopy BRDF is simulated here for three growth stages of a maize crop with the Discrete Anisotropic Radiative Transfer (DART) model in the visible and near infrared spectral bands, for two contrasted soil types (dark and bright) and different levels of heterogeneity to represent the canopy structure. 3D numerical scenes are based on in-situ structural measurements and associated BRDF simulations are thus considered as references. 1D scenarios assume either that leaves are randomly distributed (λ = 1) or clumped (λ < 1). If BRDF simulations seem globally reliable under the assumption of a random distribution in near infrared, it can also lead to relative errors on the total BRDF up to 30% in the red spectral band. It comes out that the use of a clumping index in a 1D reflectance model generally improves BRDF simulations in the red considering a bright soil, which seems relatively independent of LAI. In the near infrared, best results are usually obtained with homogeneous canopies, except with the dark soil. Clearly, influent factors are mainly the LAI and the spectral contrast between soil and leaves.     

棉花叶面积指数冠层反射率光谱响应及其反演

【目的】研究棉花冠层光谱对不同叶面积指数（LAI）的响应，建立棉花LAI光谱反演模型。【方法】利用2003～2004年采集的棉花光谱与LAI的246组数据，分析LAI与冠层反射率光谱和反射率一阶微分光谱间的定量关系。【结果】当LAI大于2.5后不同LAI棉花群体光谱反射率在可见光波段趋于饱和；LAI与可见光波段和短波红外波段（水分吸收带除外）光谱反射率呈显著负相关，与近红外波段高光谱反射率呈显著正相关；LAI与棉花反射率一阶微分光谱主要在蓝边（523～531 nm）、黄边（570～576 nm）、红边（700～755 nm）形成3个相关系数高台区，均达极显著水平，其中红边区的相关性最高。棉花红边位置固定，分别在718 nm和723 nm，且以 723 nm处对LAI更敏感。在反演棉花LAI的高光谱参数中VI (660、800)、VI (550、800)、VI (500、800)、VI (670、800)、Sdy (570～573 nm)、SDr (714～755 nm)、D723、Dr 估算LAI相对误差低于30%，RSME小于0.6，其中VI (600、800)、VI(550、800)两个参数估算水平最高，相对误差分别为21.7%与21.0%，RMSE分别为0.416与0.419；利用SDr与SDr/SDb分别对LAI大于1.0 与小于1.0 的棉花群体反演，能显著提高LAI的估算水平。【结论】应用高光谱分析方法能够提取棉花冠层特征光谱信息，构建LAI高光谱反演参数，建立估算模型，并且利用包含不同光谱参数的分段模型可以进一步提高LAI反演精度。     

应用付里叶变换近红外漫反射光谱法测定谷子中的粗脂肪含量

本文介绍了我们用FTNIRDRS方法对谷物籽粒中蛋白质含量的定量分析的研究工作。预测(谷子、玉米、小麦)方程的相关系数R谷子为0.986,玉米为0.964,小麦为0.972,标准偏差SD,谷子为±0.325,玉米为±0.39,小麦为±0.45。