Mapping crop water stress index in a ‘Pinot-noir’ vineyard: comparing ground measurements with thermal remote sensing imagery from an unmanned aerial vehicle

Characterizing the spatial variability in water status across vineyards is a prerequisite for precision irrigation. The crop water stress index (CWSI) indicator was used to map the spatial variability in water deficits across an 11-ha ‘Pinot noir’ vineyard. CWSI was determined based on canopy temperatures measured with infrared temperature sensors placed on top of well-watered and water-stressed grapevines in 2009 and 2010. CWSI was correlated with leaf water potential (Ψ_L) (R ² = 0.83). This correlation was also tested with results from high resolution airborne thermal imagery. An unmanned aerial vehicle equipped with a thermal camera was flown over the vineyard at 07:30, 09:30, and 12:30 h (solar time) on 31 July 2009. At about the same time, Ψ_L was measured in 184 grapevines. The image obtained at 07:30 was not useful because it was not possible to separate soil from canopy temperatures. Using the airborne data, the correlation between CWSI and Ψ_L had an R ² value of 0.46 at 09:30 h and of 0.71 at 12:30 h, suggesting that the latter was the more favorable time for obtaining thermal images that were linked with Ψ_L values. A sensitivity analysis of varying pixel size showed that a 0.3 m pixel was needed for precise CWSI mapping. The CWSI maps thus obtained by airborne thermal imagery were effective in assessing the spatial variability of water stress across the vineyard.     

Bioclimatological analysis of the bark necrotic rubber tree in Côte d’Ivoire plantations

The necrosis of the rubber tree is an affection of the stem, being expressed by a deterioration of the cortical tissues on the level of which are located the conducting latex tissues. We studied the water relations in a mature rubber tree plantation (clone PB 260; planted in January 1996), in Côte d’Ivoire (May and September 2004), on “healthy” and on “necrosed” trees: mean height 15.0 m; mean circumference at 1.3 m level 59.6 cm; stand density 333 trees ha^?1; leaf area index 3.2; rooting depths 4 m; field capacity, R_FC = 412 mm; permanent wilting point, R_WP = 225 mm; available water content, R_AW = 187 mm. Measurements of water potential allow us to appreciate resistances along the continuum roots-trunk-leaves: the resistances between the trunk and the leaves are identical for “healthy” trees (0.3 ± 0.1 cm³ H₂O s^?1 bars^?1) and “necrosed” trees (0.2 ± 0.1 cm³ H₂O s^?1 bars^?1); on the other hand, concerning the resistances between the roots and the trunk the two types of trees differentiate radically: 0.2 ± 0.1 cm³ H₂O s^?1 bars^?1 for “healthy” trees and 1.1 ± 0.3 cm³ H₂O s^?1 bars^?1 for “necrosed” trees, it is thus on the level of the junction of grafting that resistance is exceptionally high for “necrosed” trees. The results seem to show a difference in functioning between “healthy” and “necrosed” trees in the regulation of transpiration flux. Measurements were made here in optimum conditions for water availability; these measurements would have to be continued in conditions of water shortage for the plant, we could then provide more contrasting results.     

Identification of yellow rust in wheat using in-situ spectral reflectance measurements and airborne hyperspectral imaging

The aim of this study was to evaluate the accuracy of the spectro-optical, photochemical reflectance index (PRI) for quantifying the disease index (DI) of yellow rust (Biotroph Puccinia striiformis) in wheat (Triticum aestivum L.), and its applicability in the detection of the disease using hyperspectral imagery. Over two successive seasons, canopy reflectance spectra and disease index (DI) were measured five times during the growth of wheat plants (3 varieties) infected with varying amounts of yellow rust. Airborne hyperspectral images of the field site were also acquired in the second season. The PRI exhibited a significant, negative, linear, relationship with DI in the first season (r ² = 0.91, n = 64), which was insensitive to both variety and stage of crop development from Zadoks stage 3–9. Application of the PRI regression equation to measured spectral data in the second season yielded a coefficient of determination of r ² = 0.97 (n = 80). Application of the same PRI regression equation to airborne hyperspectral imagery in the second season also yielded a coefficient of determination of DI of r ² = 0.91 (n = 120). The results show clearly the potential of PRI for quantifying yellow rust levels in winter wheat, and as the basis for developing a proximal, or airborne/spaceborne imaging sensor of yellow rust in fields of winter wheat.     

Leaf nitrogen estimation in potato based on spectral data and on simulated bands of the VENμS satellite

Relationships between leaf spectral reflectance at 400–900 nm and nitrogen levels in potato petioles and leaves were studied. Five nitrogen (N) fertilizer treatments were applied to build up levels of nitrogen variation in potato fields in Israel in spring 2006 and 2007. Reflectance of leaves was measured in the field over a spectral range of 400–900 nm. The leaves were sampled and analyzed for petiole NO₃–N and leaf percentage N (leaf-%N). Prediction models of leaf nitrogen content were developed based on an optical index named transformed chlorophyll absorption reflectance index (TCARI) and on partial least squares regression (PLSR). Prediction models were also developed based on simulated bands of the future VENμS satellite (Vegetation and Environment monitoring on a New Micro-Satellite). Leaf spectral reflectance correlated better with leaf-%N than with petiole NO₃–N. The TCARI provided strong correlations with leaf-%N, but only at the tuber-bulking stage. The PLSR analysis resulted in a stronger correlation than TCARI with leaf-%N. An R ² of 0.95 (p < 0.01) and overall accuracy of 80.5% (Kappa = 74%) were determined for both vegetative and tuber-bulking periods. The simulated VENμS bands gave a similar correlation with leaf-%N to that of the spectrometer spectra. The satellite has significant potential for spatial analysis of nitrogen levels with inexpensive images that cover large areas every 2 days.     

Inversion of Biochemical Parameters by Selection of Proper Vegetation Index in Winter Wheat

Recent studies have demonstrated the application of vegetation indices from canopy reflected spectrum for inversion of chlorophyll concentration.Some indices are both response to variations of vegetation and environmental factors.Canopy chlorophyll concentration,an indicator of photosynthesis activity,is related to nitrogen concentration in green vegetation and serves as an indicator of the crop response to soil nitrogen fertilizer application.The combination of normalized difference vegetation index (NDVI) and photochemical reflectance index (PRI) can reduce the effect of leaf area index (LAI) and soil background.The canopy chlorophyll inversion index (CCII) was proved to be sensitive to chlorophyll concentration and very resistant to the other variations.This paper introduced the ratio of TCARI/OSAVI to make accurate predictions of winter wheat chlorophyll concentration under different cultivars.It indicated that canopy chlorophyll concentration could be evaluated by some combined vegetation indices.     

Simple method for extracting the seasonal signals of photochemical reflectance index and normalized difference vegetation index measured using a spectral reflectance sensor

A spectral reflectance sensor(SRS) fixed on the near-surface ground was developed to support the continuous monitoring of vegetation indices such as the normalized difference vegetation index(NDVI) and photochemical reflectance index(PRI). NDVI is useful for indicating crop growth/phenology, whereas PRI was developed for observing physiological conditions. Thus, the seasonal change patterns of NDVI and PRI are two valuable pieces of information in a crop-monitoring system. However, capturing the seasonal patterns is considered challenging because the vegetation index values estimated by the reflection from vegetation are often governed by meteorological conditions, such as solar irradiance and precipitation. Further, unlike growth/phenology, the physiological condition has diurnal changes as well as seasonal characteristics. This study proposed a novel filtering method for extracting the seasonal signals of SRS-based NDVI and PRI in paddy rice, barley, and garlic. First, the measurement accuracy of SRSs was compared with handheld spectrometers, and the R~2 values between the two devices were 0.96 and 0.81 for NDVI and PRI, respectively. Second, the experimental study of threshold criteria with respect to meteorological variables(i.e., insolation, cloudiness, sunshine duration, and precipitation) was conducted, and sunshine duration was the most useful one for excluding distorted values of the vegetation indices. After data processing based on sunshine duration, the R2 values between the measured vegetation indices and the extracted seasonal signals of vegetation indices increased by approximately 0.002–0.004(NDVI) and 0.065–0.298(PRI) on the three crops, and the seasonal signals of vegetation indices became noticeably improved. This method will contribute to an agricultural monitoring system by identifying the seasonal changes in crop growth and physiological conditions.     

Improving in-season estimation of rice yield potential and responsiveness to topdressing nitrogen application with Crop Circle active crop canopy sensor

In-season site-specific nitrogen (N) management is a promising strategy to improve crop N use efficiency and reduce risks of environmental contamination. To successfully implement such precision management strategies, it is important to accurately estimate yield potential without additional topdressing N application (YP₀) as well as precisely assess the responsiveness to additional N application (RI) during the growing season. Previous research has mainly used normalized difference vegetation index (NDVI) or ratio vegetation index (RVI) obtained from GreenSeeker active crop canopy sensor with two fixed bands in red and near-infrared (NIR) spectrums to estimate these two parameters. The development of three-band Crop Circle active sensor provides a potential to improve in-season estimation of YP₀ and RI. The objectives of this study were twofold: (1) identify important vegetation indices obtained from Crop Circle ACS-470 sensor for estimating rice YP₀ and RI; and (2) evaluate their potential improvements over GreenSeeker NDVI and RVI. Four site-years of field N rate experiments were conducted in 2012 and 2013 at the Jiansanjiang Experiment Station of China Agricultural University located in Northeast China. The GreenSeeker and Crop Circle ACS-470 active canopy sensor with green, red edge, and NIR bands were used to collect rice canopy reflectance data at different key growth stages. The results indicated that both the GreenSeeker (best R² = 0.66 and 0.70, respectively) and Crop Circle (best R² = 0.71 and 0.77, respectively) sensors worked well for estimating YP₀ and RI at the stem elongation stage. At the booting stage, Crop Circle red edge optimized soil adjusted vegetation index (REOSAVI, R² = 0.82) and green ratio vegetation index (R² = 0.73) explained 26 and 22 % more variability in YP₀ and RI, respectively, than GreenSeeker NDVI or RVI. At the heading stage, the GreenSeeker sensor indices became saturated and consequently could not be used for YP₀ or RI estimation, while Crop Circle REOSAVI and normalized green index could still explain more than 70 % of YP₀ and RI variability. It is concluded that both sensors performed similarly at the stem elongation stage, but significantly better results were obtained by the Crop Circle sensor at the booting and heading stages. Furthermore, the results revealed that Crop Circle green band-based vegetation indices performed well for RI estimation while the red edge-based vegetation indices were the best for estimating YP₀ at later growth stages.     

2.4GHz无线信号在苹果园中的衰减模型

为解决苹果园中无线传感器网络的规划和部署问题,研究2.4GHz无线信道在苹果园中的传播特性。在山东省肥城市普通的苹果园进行实地试验。选取对信号传播影响最大的一列果树,发射天线固定在两棵树之间发射信号,分别测量6个高度18个位置点的接收信号强度和丢包率。回归分析结果表明:无论发射天线多高,不同水平高度上的接收信号强度衰减均符合对数路径损耗模型,拟合的决定系数为0.927~0.987。发射天线高度不变时,衰减系数n值能用接收天线高度的二次函数曲线拟合,拟合的决定系数为0.71~0.89;模型参数A和接收天线高度符合线性关系,拟合的RMSE为0.2~1.2。建立以发射天线高度、接收天线高度和传播距离为参数的衰减模型并进行验证试验,结果表明:RMSE为2~5,94%的R2值大于0.9,预测模型能较好的估算收发天线高度不同时的信号强度损耗。     

Development of an in-season estimate of yield potential utilizing optical crop sensors and soil moisture data for winter wheat

When utilizing optical sensors to make in-season agronomic recommendations in winter wheat, one parameter often required is the in-season grain yield potential at the time of sensing. Current estimates use an estimate of biomass, such as normalized difference vegetation index (NDVI), and growing degree days (GDDs) from planting to NDVI data collection. The objective of this study was to incorporate soil moisture data to improve the ability to predict final grain yield in-season. Crop NDVI, GDDs that were adjusted based upon if there was adequate water for crop growth, and the amount of soil profile (0–0.80 m) water were incorporated into a multiple linear regression model to predict final grain yield. Twenty-two site-years of N fertility trials with in-season grain yield predictions for growth stages ranging from Feekes 3 to 10 were utilized to calibrate the model. Three models were developed: one for all soil types, one for loamy soil textured sites, and one for coarse soil textured sites. The models were validated with 11 independent site-years of NDVI and weather data. The results indicated there was no added benefit to having separate models based upon soil types. Typically, the models that included soil moisture, more accurately predicted final grain yield. Across all site years and growth stages, yield prediction estimates that included soil moisture had an R² = 0.49, while the current model without a soil moisture adjustment had an R² = 0.40.     

Monitoring nitrogen,phosphorus and sulphur in hybrid rice (<Emphasis Type="Italic">Oryza sativa</Emphasis> L.) using hyperspectral remote sensing

Till date, the remote sensing research on crop nutrient monitoring has focused mainly on biomass and nitrogen (N) estimation and only a few attempts had been made to characterize and monitor macronutrients other than N. Field experiments were undertaken to study the remote detection of macronutrient status of rice using hyperspectral remote sensing. The variability in soil available N, phosphorus (P) and sulphur (S) and their content in plants were created using artificial fertility gradient design. The leaf and canopy hyperspectral reflectance was captured from variable macronutrient status vegetation. Linear correlation analysis between the spectral reflectance and plant nutrient status revealed significantly (p < 0.05) higher correlation coefficient at 670, 700, 730, 1090, 1260, 1460 nm for the nutrient under study. Published and proposed vegetation indices (VIs) were tested for canopy N, P and S prediction. The results of the investigation revealed that, published VIs (NDVI hyper and NDVI broadbands) could retrieve canopy N with higher accuracy, but not P and S. The predictability of the visible and short wave infrared based VI NRI₁₅₁₀ ((R₁₅₁₀ ? R₆₆₀)/(R₁₅₁₀ + R₆₆₀)) was the highest (r = 0.81, p < 0.01) for predicting N. Based on the outcomes of linear correlation analysis new VIs were proposed for remote detection of P and S. Proposed VI P_670_1260 ((R₁₂₆₀ ? R₆₇₀)/(R₁₂₆₀ + R₆₇₀)) retrieved canopy P status with higher prediction accuracy (r = 0.67, p < 0.01), whereas significantly higher canopy S prediction (r = 0.58, p < 0.01) was obtained using VI S_670_1090 ((R₁₀₉₀ ? R₆₇₀)/(R₁₀₉₀ + R₆₇₀)). The proposed spectral algorithms could be used for real time and site-specific N, P and S management in rice. Nutrient specific wavelengths, identified in the present investigation, could be used for developing relatively low-cost sensors of hand-held instruments to monitor N, P and S status of rice plant.     

Application of day and night digital photographs for estimating maize biophysical characteristics

In this study, an inexpensive camera-observation system called the Crop Phenology Recording System (CPRS), which consists of a standard digital color camera (RGB cam) and a modified near-infrared (NIR) digital camera (NIR cam), was applied to estimate green leaf area index (LAI), total LAI, green leaf biomass and total dry biomass of stalks and leaves of maize. The CPRS was installed for the 2009 growing season over a rainfed maize field at the University of Nebraska-Lincoln Agricultural Research and Development Center near Mead, NE, USA. The vegetation indices called Visible Atmospherically Resistant Index (VARI) and two green–red–blue (2g–r–b) were calculated from day-time RGB images taken by the standard commercially-available camera. The other vegetation index called Night-time Relative Brightness Index in NIR (NRBI_NIR) was calculated from night-time flash NIR images taken by the modified digital camera on which a NIR band-pass filter was attached. Sampling inspections were conducted to measure bio-physical parameters of maize in the same experimental field. The vegetation indices were compared with the biophysical parameters for a whole growing season. The VARI was found to accurately estimate green LAI (R² = 0.99) and green leaf biomass (R² = 0.98), as well as track seasonal changes in maize green vegetation fraction. The 2g–r–b was able to accurately estimate total LAI (R² = 0.97). The NRBI_NIR showed the highest accuracy in estimation of the total dry biomass weight of the stalks and leaves (R² = 0.99). The results show that the camera-observation system has potential for the remote assessment of maize biophysical parameters at low cost.     

Comparing narrow and broad-band vegetation indices to estimate leaf chlorophyll content in planophile crop canopies

A comparison of the sensitivity of several broad- and narrow-band vegetation indices (VIs) to leaf chlorophyll content in planophile crop canopies is addressed by the analysis of a large synthetic dataset. Broad-band indices included classical slope-based VIs (i.e. NDVI—normalized difference VI and SR—simple ratio) and some indices incorporating green reflectance (i.e. Green NDVI, NIR/green ratio and the newly proposed CVI—chlorophyll vegetation index), whereas narrow-band indices included those specifically proposed to estimate leaf chlorophyll at the canopy scale (i.e. MCARI—modified chlorophyll absorption reflectance index, TCARI—transformed CARI, TCARI/OSAVI ratio—TCARI/optimized soil adjusted VI and REIP—red edge inflection position). Synthetic data were obtained from the coupled PROSPECT + SAILH leaf and canopy reflectance models in the direct mode. In addition to traditional regression-based statistics (coefficient of determination and root mean square error, RMSE), changes in sensitivity of a VI over the range of chlorophyll content were analyzed using a sensitivity function. The broad-band chlorophyll vegetation index outperformed the other VIs considered as a leaf chlorophyll estimator at the canopy scale, with the exception of the TCARI/OSAVI ratio for some soil conditions.     

沿海滩涂棉花叶片叶绿素含量高光谱遥感估算模型研究

以连云港滩涂棉花地为研究区域,利用ASD便携式光谱仪在晴朗天气条件下测试了野外采集的棉花叶片反射光谱,选取原始光谱和一阶导数光谱作为多变量,三边参数（红边、黄边和蓝边）和归一化植被指数NDVI、比值植被指数RVI、结构相关色素指数SIPI、叶面叶绿素指数LCI、水分指数WI、窄波段微分植被指数1DZ_DGVI和窄波段植被指数TCARI/OSAVI作为单变量,分析棉花叶片叶绿素含量与这些变量之间的相关性;在相关分析的基础上构建棉花叶片叶绿素含量估算模型。结果表明,叶绿素a、b和a＋b含量与单变量参数之间的相关性均未达显著水平;而与原始光谱、导数光谱都存在显著相关性。对叶绿素a含量而言,基于440 nm处的一阶导数光谱应用指数函数和幂函数构建的估算模型精度最高,R2为0.231。对叶绿素b含量而言,基于652 nm处的一阶导数光谱应用一元线性回归法构建的高光谱估算模型精度最高,R2为0.165。对叶绿素a＋b含量而言,基于440 nm处的一阶导数光谱应用指数函数、复合函数和生长函数构建的估算模型精度高,R2为0.155。该研究为进一步加强滩涂农业管理和提高滩涂农作物的产量提供技术支持。     

Estimation of bioenergy crop yield and N status by hyperspectral canopy reflectance and partial least square regression

The objective of this study was to compare performance of partial least square regression (PLSR) and best narrowband normalize nitrogen vegetation index (NNVI) linear regression models for predicting N concentration and best narrowband normalize different vegetation index (NDVI) for end of season biomass yield in bioenergy crop production systems. Canopy hyperspectral data was collected using an ASD FieldSpec FR spectroradiometer (350–2500 nm) at monthly intervals in 2012 and 2013. The cropping systems evaluated in the study were perennial grass {mixed grass [50 % switchgrass (Panicum virgatum L.), 25 % Indian grass “Cheyenne” (Sorghastrum nutans (L.) Nash) and 25 % big bluestem “Kaw” (Andropogon gerardii Vitman)] and switchgrass “Alamo”} and high biomass sorghum “Blade 5200” (Sorghum bicolor (L.) Moench) grown under variable N applications rates to estimate biomass yield and quality. The NNVI was computed with the wavebands pair of 400 and 510 nm for the high biomass sorghum and 1500 and 2260 nm for the perennial grass that were strongly correlated to N concentration for both years. Wavebands used in computing best narrowband NDVI were highly variable, but the wavebands from the red edge region (710–740 nm) provided the best correlation. Narrowband NDVI was weakly correlated with final biomass yield of perennial grass (r² = 0.30 and RMSE = 1.6 Mg ha^?1 in 2012 and r² = 0.37 and RMSE = 4.0 Mg ha^?1, but was strongly correlated for the high biomass sorghum in 2013 (r² = 0.72 and RMSE = 4.6 Mg ha^?1). Compared to the best narrowband VI, the RMSE of the PLSR model was 19–41 % lower for estimating N concentration and 4.2–100 % lower for final biomass. These results indicates that PLSR might be best for predicting the final biomass yield using spectral sample obtained in June to July, but narrowband NNVI was more robust and useful in predicting N concentration.     

基于光谱指数的棉花黄萎病叶片氮素含量提取

[目的]研究棉花黄萎病叶片氮素含量与高光谱的关系,以期用简便、无损的遥感技术提取病害棉叶氮素含量,为大面积遥感监测棉花病害提供理论依据.[方法]通过小区和大田同步调查棉花黄萎病,在不同生育期测定病叶光谱及其氮素含量.将病叶光谱特征参数与氮素含量进行相关分析,建立病叶氮素含量估测模型并检验.[结果]随着病害严重度的增加,棉叶氮素含量逐渐减小.病叶氮素含量与光谱指数FD731、NDVI[670,890]、DVI[FD554,FD731]、PVI[FD554,FD731]、RDVI[702,758]、RDVI[FD554,FD731]、SAVI、OSAVI、PRI[570,531]、PRI[702,758]、REP、Lo、Depth672和Area672呈极显著正相关,与R550、R680、R702、SD737、DVI[450,560]、NDVI[702,758]、DVI[702,758]、RVI[702,758]、SIPI、TCARI、CCII、PPR[550,450]、Lwidth和ND672均呈极显著负相关,与Dr未达显著相关.选取相关系数较大的光谱参数建立的病叶氮素含量估测模型均达到显著水平,整体上利用DVI[702,758]、PVI[FD554,FD731]和NDVI[702,758]进行氮素含量的估测精度最高,模型的预测的相对误差均小于2;.[结论]考虑到DVI[702,758]建立的模型更为实用,可作为病害棉叶氮素含量的最佳估测模型.     

基于高光谱遥感的冬小麦叶水势估算模型

【目的】采用高光谱技术,建立快速、无损与准确获取冬小麦叶水势的估算模型,为小麦灌溉的精确管理提供科学依据。【方法】利用不同水分处理的大田试验,于小麦主要生育期同步测定冠层光谱反射率、叶水势、土壤水分等信息,并探讨高光谱植被指数与冬小麦叶水势之间的定量关系。通过相关性分析、回归分析等方法,基于不同水分处理,构建4种植被指数与冬小麦叶水势的估算模型。【结果】不同水分处理和不同生育期的冬小麦,其冠层光谱反射率具有显著的变化特征。在可见光波段,冬小麦冠层反射率随着水分含量的增加而逐渐降低,而在近红外波段,其冠层反射率则随着土壤水分含量的增加而升高。随着小麦生育期的推进,在近红外波段,抽穗期的冠层反射率比拔节期的高,在灌浆期之后,红波段(670 nm)、蓝波段(450 nm)的反射率上升加快;4种植被指数与叶水势显著相关(P0.05),相关系数|r|均在0.711以上,四者均可用于冬小麦叶片水势的定量监测。在充分供水条件下(70%FC),植被指数OSAVI和EVI2与叶水势的相关系数|r|(分别为0.75和0.771)均低于植被指数NDVI和RVI与叶水势的相关系数|r|(分别为0.808和0.896),而在重度水分亏缺条件下(50%FC),植被指数OSAVI和EVI2与叶水势的相关系数|r|(分别为0.857和0.853)均高于植被指数NDVI和RVI与叶水势的相关系数|r|(分别为0.711和0.792);所建模型对45个未知样的预测结果与实测值相似度较高,其回归模型R~2、验证模型MRE、RMSE的范围分别为0.616—0.922、-17.50%—-12.52%、0.102—0.133。在70%FC水分处理下,基于EVI2(enhanced vegetation index)所得叶水势估算模型的R~2最高,为0.922,而在60%FC和50%FC水分处理下,由于考虑了土壤背景的影响,基于OSAVI所建模型的R~2最高,分别为0.922和0.856。【结论】4种植被指数均可用于冬小麦叶水势的定量监测。但是,在构建不同水分处理的叶水势估算模型时,应考虑土壤背景对冠层光谱的影响。研究结果可以为小麦精准灌溉管理提供技术依据,为星载数据的参数反演提供模型支持。     

Use of hyperspectral data to assess the effects of different nitrogen applications on a potato crop

This study was conducted to explore whether hyperspectral data could be used to discriminate between the effects of different rates of nitrogen application to a potato crop. The field experiment was carried out in the Central Potato Research Station, Jalandhar, on seven plots with different nitrogen (N) treatments. Spectral reflectance was measured using a 512-channel spectroradiometer with a range of 395–1075 nm on two different dates during crop growth. An optimum number of bands were selected from this range based on band–band r ², principal component analysis and discriminant analysis. The four bands that could discriminate between the rates of N applied were 560, 650, 730, and 760 nm. An ANOVA analysis of several narrow-band indices calculated from the reflectance values showed the indices that were able to differentiate best between the different rates of N application. These were reflectance ratio at the red edge (R_740/720) and the structure insensitive pigment index (SIPI). To estimate leaf N, reflectance ratios were determined for each band combination and were evaluated for their correlation with the leaf N content. A regression model for N estimation was obtained using the reflectance ratio indices at 750 and 710 nm wavelengths (F-ratio = 32 and r ² = 0.551, P < 0.000).     

Methodology for measuring fAPAR in crops using a combination of active optical and linear irradiance sensors: a case study in Triticale (X Triticosecale Wittmack)

The amount of photosynthetically active radiation (PAR, 0.4–0.7 μm) absorbed by plants for photosynthesis relative to incident radiation is defined as the fraction of absorbed photosynthetically active radiation (fAPAR). This is an important variable in both plant biomass production and plant growth modeling. This study investigates the application of a newly developed, linear irradiance sensor (LightScout Quantum Bar Sensor, LightScout, Spectrum Technologies, Inc. USA), to quantify fAPAR for a demonstrator crop, Triticale (X Triticosecale Wittmack). A protocol was devised for sensor placement to determine reflected PAR components of fAPAR and to determine the optimal time of day and sensor orientation for data collection. Coincident, top of canopy, normalized difference vegetation index (NDVI) measurements were also acquired with a CropCircle? ACS-210 sensor and measurements correlated with derived fAPAR values. The optimum height of the linear irradiance sensor above soil or plant canopy was found to be 0.4 m while measuring reflected PAR. Measurement of fAPAR was found to be stable when conducted within 1 h of local solar noon in order to avoid significant bidirectional effects resulting from diurnal changes of leaf orientation relative to the vertically-placed sensor. In the row crop studied, averaging fAPAR readings derived from the linear irradiance sensor orientated across and along the plant row provided an R² = 0.81 correlation with above-canopy NDVI. Across row sensor orientation also gave a similar correlation of R² = 0.76 allowing the user to reduce sampling time.     

Predicting cover crop biomass by lightweight UAS-based RGB and NIR photography: an applied photogrammetric approach

Easy-to-capture and robust plant status indicators are important factors when implementing precision agriculture techniques on fields. In this study, aerial red, green and blue color space (RGB) photography and near-infrared (NIR) photography was performed on an experimental field site with nine different cover crops. A lightweight unmanned aerial system (UAS) served as platform, consumer cameras as sensors. Photos were photogrammetrically processed to orthophotos and digital surface models (DSMs). In a first validation step, the spatial precision of RGB orthophotos (x and y, ± 0.1 m) and DSMs (z, ± 0.1 m) was determined. Then, canopy cover (CC), plant height (PH), normalized differenced vegetation index (NDVI), red edge inflection point (REIP), and green red vegetation index (GRVI) were extracted. In a second validation step, the PHs derived from the DSMs were compared with ground truth ruler measurements. A strong linear relationship was observed (R ² = 0.80?0.84). Finally, destructive biomass samples were taken and compared with the remotely-sensed characteristics. Biomass correlated best with plant height (PH), and good approximations with linear regressions were found (R ² = 0.74 for four selected species, R ² = 0.58 for all nine species). CC and the vegetation indices (VIs) showed less significant and less strong overall correlations, but performed well for certain species. It is therefore evident that the use of DSM-based PHs provides a feasible approach to a species-independent non-destructive biomass determination, where the performance of VIs is more species-dependent.     

Analysis of spectral difference between the foreside and backside of leaves in yellow rust disease detection for winter wheat

Disease detection by means of hyperspectral reflectance is inevitably influenced by the spectral difference between foreside (adaxial surface) and backside (abaxial surface) of a leaf. Taking yellow rust disease in winter wheat as an example, the spectral differences between the foreside and backside of healthy and diseased wheat leaves at both jointing stage and grain filling stage were investigated based on spectral measurements with a large sample size. The spectral difference between leaf orientations was found to be confused with disease signals to some extent. Firstly, the original bands and spectral features (SFs) that were sensitive to the disease were identified through a correlation analysis. Then, to eliminate the influence of leaf orientation, a pairwise t test was used to screen for the orientation insensitive bands and SFs. By conducting an overlapping procedure, the bands/SFs that were sensitive to the disease yet insensitive to the leaf orientations were selected and tested for disease detection. The results suggested that the Ref_{525–745 nm}, Ref_{1060–1068 nm}, DEP920–1120, DEP1070–1320, AREA1070–1320, SR and NDVI at the jointing stage, and the Ref_{606–697 nm}, Ref_{740–752 nm}, WID550–770, SR, NDVI, GNDVI, RDVI, GI and MCARI at the grain filling stage were capable of eliminating the influence of leaf orientation, and were retained for disease detection. Given these features, models based on the partial least square regression analysis showed a better performance at the grain filling stage, with the R ² of 0.854 and RMSE of 0.104. This result indicated that reliable estimation of disease severity can be made until the grain filling stage. In the future, more attention should be given to leaf orientation when detecting disease at the canopy level.