Development of an optical sensor for crop leaf chlorophyll content detection

Nitrogen content in crop leaf is an important indication for evaluating crop health and predicting crop yield. A normalized difference vegetation index (NDVI) is widely used as an indicator in estimating leaf nitrogen content in practice. How to effectively and accurately measure the NDVI value of crop leaves in the field is a challenge on in-field use instrument development. This paper reports the development of a hand-held spectroscopy-based optical sensing device for measuring crop leaf NDVI values under in-field natural light conditions. This optical sensing device could simultaneously measure the spectral reflectance of canopies and the solar intensity at two bands of 610 and 1220 nm, and calculate NDVI value in real-time based on measured spectral reflectance. This device was tested in tomato plants chlorophyll content measurement. A series of field tests were conducted to evaluate the performance of the sensor, and tomato leaf samples were collected for measuring chlorophyll contents as the reference for validation. Obtained results indicated that NDVI values measured with this sensing device had a close correlation with chlorophyll contents of the collected leaf samples measured in laboratory with a UV–vis spectrophotometer .     

Quantitative analysis of minerals and electric conductivity of red grape homogenates by near infrared reflectance spectroscopy

The use of near infrared (NIR) reflectance spectroscopy to measure the concentration of minerals and electric conductivity (EC) in red grape homogenates was investigated. Wine grape samples (n = 209) from two vintages, representing a wide range of varieties and regions were analysed by Inductively Coupled Plasma Optical Emission Spectrometry (ICPOES) for the concentrations of calcium (Ca), potassium (K), magnesium (Mg), phosphorus (P), sulphur (S), iron (Fe), and manganese (Mn) and scanned in reflectance in a NIR instrument (400-2500 nm). The spectra were pre-processed using multiple scatter correction (MSC) before developing the calibration models using partial least squares (PLS) regression and cross validation. Coefficients of determination in cross validation (R²) and the standard errors of cross validation (SECV) obtained were for Fe (0.60 and 1.49 mg kg⁻¹), Mn (0.71 and 0.41 mg kg⁻¹), Ca (0.75 and 60.89 mg kg⁻¹), Mg (0.84 and 12.93 mg kg⁻¹), K (0.78 and 285.34 mg kg⁻¹), P (0.70 and 40.19 mg kg⁻¹), S (0.88 and 14.45 mg kg⁻¹) and EC (0.87 and 7.66 mS). The results showed that Mg, S and EC in grape berries might be measured by NIR reflectance spectroscopy.     

Prediction of yield and the contribution of legumes in legume-grass mixtures using field spectrometry

Productivity and botanical composition of legume-grass swards in rotation systems are important factors for successful arable farming in both organic and conventional farming systems. As these attributes vary considerably within a field, a non-destructive method of detection while doing other tasks would facilitate more targeted management of crops and nutrients in the soil–plant–animal system. Two pot experiments were conducted to examine the potential of field spectroscopy to assess total biomass and the proportions of legume, using binary mixtures and pure swards of grass and legumes. The spectral reflectance of swards was measured under artificial light conditions at a sward age ranging from 21 to 70 days. Total biomass was determined by modified partial least squares (MPLS) regression, stepwise multiple linear regression (SMLR) and the vegetation indices (VIs) simple ratio (SR), normalized difference vegetation index (NDVI), enhanced vegetation index (EVI) and red edge position (REP). Modified partial least squares and SMLR gave the largest R ² values ranging from 0.85 to 0.99. Total biomass prediction by VIs resulted in R ² values of 0.87–0.90 for swards with large leaf to stem ratios; the greatest accuracy was for EVI. For more mature and open swards VI-based detection of biomass was not possible. The contribution of legumes to the sward could be determined at a constant biomass level by the VIs, but this was not possible when the level of biomass varied.     

Extended-altitude, aerial mapping of crop NDVI using an active optical sensor: A case study using a Raptor™ sensor over wheat

Recently reported testing of active, optical crop sensors in low-level aircraft have demonstrated a new class of airborne sensing system that can be deployed under any ambient illumination conditions, including at night. A second-generation, high-powered, light-emitting diode system has been assembled and tested over a 80 ha field of wheat (Triticum aestevum) by mapping the normalised difference vegetation index (NDVI) at altitudes ranging from 15 to 45 m above the canopy; significantly higher altitudes than existing systems. Comparisons with a detailed on-ground NDVI survey indicated the aerial sensor values were highly correlated to the on-ground sensor (0.79 < R² < 0.85), with close to unity slope and zero offset. The maximum average deviation between aerial and on-ground NDVI values was 0.04. Sample calculations involving two exemplar algorithms, one for estimating monoculture pasture biomass and the other for estimating wheat yield, indicate such deviations to have no significant effect on prediction accuracy. The subsequent NDVI maps proved to be invariant to sensor height over the 15-45 m altitude range meaning this new sensor configuration can be deployed over undulating crops and pastures and in areas with nearby obstacles such as trees and buildings.     

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.     

基于数码相机的玉米冠层SPAD遥感估算

【目的】叶绿素是植物光合作用中重要的色素。利用作物光谱信息对叶绿素含量进行反演,为作物的实时监测和生长状态诊断提供重要依据。【方法】以大田环境下不同氮肥水平(0,50%和100%)的开花期玉米为研究对象,利用轻小型无人机搭载数码相机,获取试验区RGB影像。使用土壤调整植被指数(soil adjusted vegetation index,SAVIgreen)对图像进行分割,基于分割前后的影像分别提取15种常见的可见光植被指数,综合分析指数与玉米冠层叶绿素相对含量SPAD值的相关关系。采用单变量回归模型、多元逐步回归模型和随机森林(random forest,RF)回归算法构建玉米SPAD值的遥感估算模型,通过模型精度评价指标决定系数(coefficient of determination,R2)、均方根误差(root mean square error,RMSE)、平均相对误差(mean relative error,MRE)和显著性检验水平(P0.01),确定最佳指标和最优模型。【结果】基于分割前后的数码影像提取的VIplot和VIplant植被指数与玉米冠层SPAD值之间具有显著的相关关系,其中VIplant中的红光标准化值(NRI)、归一化叶绿素比值植被指数(NPCI)、蓝红比值指数(BRRI)、差值植被指数(DVI)与SPAD值的相关性在0.77以上;以相关性高于0.77的VIplant指数NRI、NPCI、BRRI、DVI构建的线性、指数、对数、二次多项式、幂函数的单变量回归模型中,NRI指数构建的二次多项式模型效果最好,决定系数R2为0.7976,RMSE为4.31,MRE为5.91%。在VIplant指数NRI、NPCI、BRRI、DVI参与建立的多变量SPAD反演模型中,使用随机森林方法的模型精度最高,决定系数R2为0.8682,RMSE为3.92,MRE为4.98%,而多元逐步回归模型的精度高于任意单变量回归模型,决定系数R2为0.819,RMSE为4,MRE为5.67%;对数码影像结合各模型制作的SPAD分布图进行精度分析,使用随机森林回归模型对SPAD的估测值与实测值最为接近,具有最佳的预测效果,R2为0.8247,RMSE为4.3,MRE为5.36%,可以作为玉米冠层叶绿素信息监测的主要方法。【结论】本研究证明将数码相机影像提取的可见光植被指数应用于玉米叶绿素相对含量的估测是可行的,这也为无人机遥感系统在农业方面的应用增添了新的手段和经验。     

Aerodynamic analysis and CFD simulation of several cellulose evaporative cooling pads used in Mediterranean greenhouses

The present work makes an aerodynamic analysis and computational fluid dynamics (CFD) simulation of the four commercial models of corrugated cellulose evaporative cooling pads that are most widely used in Mediterranean greenhouses. The geometric characteristics of the pads have been determined as well as the volume of water they retain at different flows of water, thus obtaining the mean thickness of the sheet of water which runs down them and their porosity. By means of low velocity wind tunnel experiments, the pressure drop produced by the pads has been recorded at different wind speeds and water flows. In this way it has been possible to obtain the relationship of the permeability and the inertial factor with pad porosity using a cubic type equation. Finally, a CFD simulation with a 3D model has been carried out for both dry pads (Q_w = 0 l s⁻¹ m⁻²) and wet ones (Q_w = 0.256 l s⁻¹ m⁻²), finding good correlation between the simulated and experimental pressure drop, with maximum differences of 9.08% for dry pads and 15.53% for wet ones at an airspeed of 3 m s⁻¹.     

Comparison of passive and active canopy sensors for the estimation of vine biomass production

Recent advances in optical designs and electronic circuits have allowed the transition from passive to active proximal sensors. Instead of relying on the reflectance of natural sunlight, the active sensors measure the reflectance of modulated light from the crop and so they can operate under all lighting conditions. This study compared the potential of active and passive canopy sensors for predicting biomass production in 25–32 randomly selected positions of a Merlot vineyard. Both sensors provided estimates of the normalized difference vegetation index (NDVI) from a nadir view of the canopy at veraison that were good predictors of pruning weight. Although the red NDVI of the passive sensors explained more of the variation in biomass (R ² = 0.82), its relationship to pruning weight was nonlinear and was best described by a quadratic regression (NDVI = 0.55 + 0.50 wt−0.21 wt²). The theoretically greater linearity of the amber NDVI-biomass relationship could not be verified under conditions of high biomass. The linear correlation to stable isotope content in leaves (¹³C and ¹⁵N) provided evidence that canopy reflectance detected plant stresses as a result of water shortage and limited fertilizer N uptake. Thus, the canopy reflectance data provided by these mobile sensors can be used to improve site-specific management practices of vineyards.     

Utility of Remote Sensing in Predicting Crop and Soil Characteristics

Remote sensing during the production season can provide visual indications of crop growth along with the geographic locations of those areas. A grid coordinate system was used to sample cotton and soybean fields to determine the relationship between spectral radiance, soil parameters, and cotton and soybean yield. During the 2 years of this study, mid- to late-season correlation coefficients between spectral radiance and yield generally ranged from 0.52 to 0.87. These correlation coefficients were obtained using the green–red ratio and a vegetation index similar to the normalized difference vegetation index (NDVI) using the green and red bands. After 102 days after planting (DAP), the ratio vegetation index (RVI), difference vegetation index (DVI), NDVI, and soil-adjusted vegetation index (SAVI) generally provided correlation coefficients from 0.54 to 0.87. Correlation coefficients for cotton plant height measurements taken 57 and 66 DAP during 2000 ranged from 0.51 to 0.76 for all bands, ratios, and indices examined, with the exception of Band 4 (720nm). The most consistent correlation coefficients for soybean yield were obtained 89–93 DAP, corresponding to peak vegetative production and early pod set, using RVI, DVI, NDVI, and SAVI. Correlation coefficients generally ranged from 0.52 to 0.86. When the topographic features and soil nutrient data were analyzed using principal component analysis (PCA), the interaction between the crop canopy, topographic features, and soil parameters captured in the imagery allowed the formation of predictive models, indicating soil factors were influencing crop growth and could be observed by the imagery. The optimum time during 1999 and 2000 for explaining the largest amount of variability for cotton growth occurred during the period from first bloom to first open boll, with R values ranging from 0.28 to 0.70. When the PCA-stepwise regression analysis was performed on the soybean fields, R ² values were obtained ranging from 0.43 to 0.82, 15 DAP, and ranged from 0.27 to 0.78, 55–130 DAP. The use of individual bands located in the green, red, and NIR, ratios such as RVI and DVI, indices such as NDVI, and stepwise regression procedures performed on the cotton and soybean fields performed well during the cotton and soybean production season, though none of these single bands, ratios, or indices was consistent in the ability to correlate well with crop and soil characteristics over multiple dates within a production season. More research needs to be conducted to determine whether a certain image analysis method will be needed on a field-by-field basis, or whether multiple analysis procedures will need to be performed for each imagery date in order to provide reliable estimates of crop and soil characteristics.     

EM38 for volumetric soil water content estimation in the root-zone of deep vertosol soils

Electromagnetic induction sensors, such as EM38, are used widely for monitoring and mapping soil attributes via the apparent electrical conductivity (EC_a) of the soil. The sensor response is the depth-integrated combination of the depth-response function of the EM38 and ‘local’ electrical conductivity (EC_az) at depth. In deep, Vertosol soils, assuming the instrument depth-response function is not perturbed by the soil and where volumetric moisture content at depth (θ_v(z)) dominates EC_az, EM38 should be capable of predicting average moisture content without recourse to mathematically complicated, and unstable profile inversion processes. Firstly a multi-height EM38 experiment was conducted over deep Vertosol soils to confirm the veracity of the EM38 depth-response function and test the concomitant hypothesis of the EM38 response being an integrated (i.e. additive) combination of depth-response function and θ_v(z). Secondly, depth profiles of moisture content were used to calibrate the EM38 to infer average θ_v(z) within the ‘root-zone’ of crop plants—here taken to be surface—0.8 m and surface—1.2 m. EM38 calibration was performed using soil samples acquired from both extracted cores and excavated pits. Mathematical summation of measured θ_v(z) from sectioned cores and the known depth-response function of the EM38 was found to explain 99% and 97% of the variance in measured EC_a for horizontal and vertical dipole configurations at multiple sensor heights above the ground. Average θ_v from surface to 0.8 m () and surface to 1.2 m () explained only 37% and 46% of the variance in on-ground EC_a for vertical dipole configuration measurements compared to 55% and 56% of the variance for horizontal dipole configuration. In a separate validation experiment, the shape of the vertical moisture profile proved highly influential in determining the ability of the calibration equations to infer underlying average moisture content, especially where the depth profile shapes differed between sensor calibration and subsequent field validation (for example following rainfall or irrigation).     

A new integrated CFD modelling approach towards air-assisted orchard spraying. Part I. Model development and effect of wind speed and direction on sprayer airflow

The current trend in modelling flow phenomena within trees such as in orchards follows the assumption of the space occupied by the trees as a porous and horizontally homogeneous medium to avoid the flow details associated with the individual plants. This being sufficient at a larger field or regional scale much has to be done at a plant scale to analyse the flow details within the plant and its elements especially for sensitive agricultural operations such as spraying. This article presents an integrated 3D computational fluid dynamics (CFD) model of airflow from a two-fan air-assisted cross-flow orchard sprayer through non-leafed orchard pear trees of 3 m average height. In this model the effect of the solid part of the canopy on airflow was modelled by directly introducing the actual 3D architecture of the canopy into the CFD model. The effect of small canopy parts, such as very short and thin branches and flowers that were not incorporated in the geometrical model, on airflow was simulated by introducing source-sink terms in the Reynolds averaged Navier-Stokes (RANS) momentum and k-? turbulence equations in a sub-domain created around the branches. This model was implemented in a CFD code of ANSYS-CFX-11.0 (ANSYS, Inc., Canonsburg, PA, USA). In this work it was possible to link the real 3D architecture of pear canopy into a CFD code of CFX. The model was able to capture the local effects of the canopy and its parts on wind and sprayer airflow directly by inserting the tree structure into the model which gave realistic results. The model showed that within the injection region of the sprayer there was an average reduction of the jet velocity by 1 m s⁻¹ for a distance of 2.3 m from the sprayer outlet due to the presence of leafless pear canopy. This reduction was variable at different vertical positions due to the difference in the canopy density. Maximal effect of the canopy was observed in the middle height of the trees between 0.25 m and 2.5 m which is the denser region with a bunch of several branches. The maximum velocity difference observed between these two positions was 1.35 m s⁻¹ at 1.75 m height. Thus, regions of high and low air velocity zones of the sprayer due to the variable branch density of the pear tree were predicted. The effects of wind speed and direction on the air jet from the sprayer were investigated using the model. For a cross- (direction of 90°) wind speed of 5 m s⁻¹ there was about 2 m s⁻¹ reduction in the sprayer jet velocity at the jet centre and 0.5 m horizontal shift of the jet centre towards the wind direction. Generally there was a decrease in the jet velocity with increasing cross-wind and decreasing wind direction with respect to the jet direction.     

Spectral and spatial differences in response of vegetation indices to nitrogen treatments on apple

Handheld chlorophyll sensors and remote sensing are two nondestructive approaches for estimating plant nitrogen (N) status, which are now commercially available. In this paper we address three questions on the application of these technologies in perennial fruit trees: (1) can individual leaf meter measurements be used to predict N status for surrounding trees?, (2) are narrow band indices more sensitive than the normalized difference vegetation index (NDVI) to differences in plant N?, and (3) is NDVI from satellite remote sensing correlated to leaf level vegetation indices? We evaluated data from a N rate trial conducted in a commercial Fuji apple orchard (Malus domestica Borkh. cv. ‘Fuji’). SPAD and CM1000 handheld chlorophyll meters and reflectance measurements using a portable spectrometer were made on individual leaves three or four times during each growing season. The reflectance measurements were used to determine NDVI and three narrow band vegetation indices. Satellite imagery from the Quickbird sensor was acquired two or three times during each growing season and used to generate NDVI for individual trees. The leaf meter measurements and vegetation indices were compared with the N application rate and plant N status measured as total leaf tissue N.We evaluated how well single leaf meter measurements predict N status for surrounding trees by calculating the differences between actual and estimated N applications from individual measurements. On average, a sample of 12 leaves (from the same treatment and same measurement date) resulted in an estimation error of 30 kg ha⁻¹ for either the SPAD or the CM1000 sensor, representing almost half of the range in N treatment rates. To evaluate any improvement in prediction of applied N using narrow band indices, we used analysis of variance (ANOVA) to compare three narrow band indices with the leaf meters and NDVI measured at leaf and canopy levels. Two narrow band indices, red edge vegetation stress index (RVSI) and modified chlorophyll absorption in reflectance index (MCARI) had higher F-values (31 and 41, respectively) than did NDVI from leaf level measurements (26), from satellite NDVI (6), or the CM1000 chlorophyll meter (12). The ANOVA results support improvements in leaf sensors using index values other than NDVI. We found that NDVI from satellite imagery acquired close to the leaf level measurement dates were positively correlated to the chlorophyll sensors and vegetation indices. When the data was averaged to the experiment plot level (twelve leaves total), the correlation coefficients between the satellite NDVI and the other sensors ranged from 0.68 for NDVI from leaf level reflectance to 0.84 with the CM1000 chlorophyll meter. Given the level of correlations, remote sensing might be a useful tool to extrapolate handheld measurements spatially throughout an orchard.     

基于Landsat影像的崇明岛东滩土壤盐分遥感反演技术

目前我国土地资源面临着严重的盐碱退化问题。以上海市崇明岛东滩盐碱土为研究对象,基于野外实地调查土壤盐分数据以及Landsat遥感影像数据计算获取的各波段反射率、盐分指数（salinity index, SI)、盐分指数1（salinity index 1, SI1）、归一化差分植被指数（normalized difference vegetation index, NDVI）、冠层盐分响应指数（canopy response salinity index, CRSI）和陆地表面水分指数（land surface water index, LSWI）,采用多元样条自回归模型（multivariate adaptive regression splines, MARS）与偏最小二乘回归方法（partial least squares regression, PLSR）分别建立土壤盐分的回归模型,并对区域盐碱土的空间格局进行探究。结果表明:①滨海土壤盐分在近红外波段有明显的吸收作用,与近红外波段、短波红外波段和NDVI相关系数较高;②MARS模型较PLSR模型对于样点土壤盐分反演有更好的效果（R2分别为0.74和0.70）;③崇明东滩滨海土壤盐分在空间上具有较高的异质性,水体附近和滩涂土壤盐分较高,林地和农田土壤盐分较低。该结果为滨海地区区域尺度上的土壤盐碱化监测提供范例,为滨海土壤盐渍化的治理及岛屿的生态建设提供参考依据。     

Development of model based sensors for the supervision of a solar dryer

Solar dryers are increasingly used in developing countries as an alternative to drying in open air, however the inherent variability of the drying conditions during day and along year drive the need for achieving low cost sensors that would enable to characterize the drying process and to react accordingly. This paper provides three different and complementary approaches for model based sensors that make use of the psychrometric properties of the air inside the drying chamber and the temperature oscillations of the wood along day. The simplest smart sensor, Smart-1, using only two Sensirion sensors, allows to estimate the accumulated water extracted from wood along a complete drying cycle with a correlation coefficient of 0.97. Smart-2 is a model based sensor that relays on the diffusion kinetics by means of assesing temperature and relative humidity of the air inside the kiln. Smart-2 model allows to determine the diffusivity, being the average value of D for the drying cycle studied equal to 5.14 × 10⁻¹⁰ m² s⁻¹ and equal to 5.12 ×  10⁻¹⁰ m² s⁻¹ for two experiments respectively. The multidistributed supervision of the dryer shows up the lack of uniformity in drying conditions supported by the wood planks located in the inner or center of the drying chamber where constant drying rate kinetics predominate. Finally, Smart-3 indicates a decreasing efficiency along the drying process from 0.9 to 0.2     

Canopy reflectance, thermal stress, and apparent soil electrical conductivity as predictors of within-field variability in grain yield and grain protein of malting barley

Three consecutive crops of malting barley grown during 2002–2004 on clay-loam on a Swedish farm (59°74’ N, 17°00’ E) were monitored for canopy reflectance at growth stages GS32 (second node detectable) and GS69 (anthesis complete), and the crops were sampled for above ground dry matter and nitrogen content. GPS-positioned unfertilised plots were established and used for soil sampling. At harvest, plots of 0.25 m² were cut in both fertilised and unfertilised plots, and 24 m² areas were also harvested from fertilised barley. The correlations between nine different vegetation indices (VIs) from each growth stage and yield and grain protein were tested. All indices were significantly correlated (at 5% level) with grain yield (GY), and protein when sampled at GS69 but only four when sampled at GS32. Three variables (the best-correlated vegetation index sampled at GS32; an index for accumulated elevated daily maximum temperatures for the grain filling period, and normalised apparent electrical conductivity (ECa) of the soil) were sufficient input in the final regressions. Using these three variables, it was possible to make either one multivariate (PLS) regression model or two linear multiple regression models for grain yield (GY) and grain protein, with correlation coefficients of 0.90 and 0.73 for yield and protein, respectively.     

Leaf chlorophyll content retrieval of wheat by simulated RapidEye,Sentinel-2 and EnMAP data

Leaf chlorophyll content(LCC) is an important physiological indicator of the actual health status of individual plants. An accurate estimation of LCC can therefore provide valuable information for precision field management. Red-edge information from hyperspectral data has been widely used to estimate crop LCC. However, after the advent of red-edge bands in satellite imagery, no systematic evaluation of the performance of satellite data has been conducted. Toward this end, we analyze herein the performance of winter wheat LCC retrieval of currant and forthcoming satellites(RapidEye, Sentinel-2 and EnMAP) and their new red-edge bands by using partial least squares regression(PLSR) and a vegetation-indexbased approach. These satellite spectral data were obtained by resampling ground-measured hyperspectral data under various field conditions and according to specific spectral response functions and spectral resolution. The results showed: 1) This study confirmed that RapidEye, Sentinel-2 and EnMAP data are suitable for winter wheat LCC retrieval. For the PLSR approach, Sentinel-2 data provided more accurate estimates of LCC(R2=0.755, 0.844, 0.805 for 2002, 2010, and 2002+2010) than do RapidEye data(R2=0.689, 0.710, 0.707 for 2002, 2010, and 2002+2010) and EnMAP data(R2=0.735, 0.867, 0.771 for 2002, 2010, and 2002+2010). For index-based approaches, the MERIS terrestrial chlorophyll index, which is a vegetation index with two red-edge bands, was the most sensitive and robust index for LCC for both the Sentinel-2 and EnMAP data(R2≥0.628), and the indices(NDRE1, SRRE1 and CIRE1) with a single red-edge band were the most sensitive and robust indices for the RapidEye data(R2≥0.420); 2) According to the analysis of the effect of the wavelength and number of used red-edge spectral bands on LCC retrieval, the short-wavelength red-edge bands(from 699 to 734 nm) provided more accurate predictions when using the PLSR approach, whereas the long-wavelength red-edge bands(740 to 783 nm) gave more accurate predictions when using the vegetation indice(VI) approach. In addition, the prediction accuracy of RapidEye, Sentinel-2 and EnMAP data was improved gradually because of more number of red-edge bands and higher spectral resolution; VI regression models that contain a single or multiple red-edge bands provided more accurate predictions of LCC than those without red-edge bands, but for normalized difference vegetation index(NDVI)-, simple ratio(SR)-and chlorophyll index(CI)-like index, two red-edge bands index didn't significantly improve the predictive accuracy of LCC than those indices with a single red-edge band. Although satellite data with higher spectral resolution and a greater number of red-edge bands marginally improve the accuracy of estimates of crop LCC, the level of this improvement remains insufficient because of higher spectral resolution, which results in a worse signal-to-noise ratio. The results of this study are helpful to accurately monitor LCC of winter wheat in large-area and provide some valuable advice for design of red-edge spectral bands of satellite sensor in future.     

A new integrated CFD modelling approach towards air-assisted orchard spraying—Part II: Validation for different sprayer types

A computational fluid dynamics (CFD) model to simulate airflow from air-assisted orchard sprayers through pear canopies was validated for three different sprayers; single-fan (Condor V), two-fan (Duoprop) and four-fan sprayers (AirJet Quatt). The first two sprayers are widely used in Belgium and the latter one is a new design. Validation experiments were carried out in an experimental orchard (pcfruit, Velm, Belgium) in spring 2008. Ultrasonic anemometers were used to measure the time-averaged velocity components at different vertical positions before the tree and after the tree when the sprayers were driven through the orchard. The model was able to predict accurately the peak jet velocity, U_m from all the sprayers considered at all distances from the sprayer centre and vertical positions. More than 95% of the local relative errors of U_m were below 20%. Average relative errors, E, and root mean square errors, E_RMS, were all less than 11.04% and 1.68 m s⁻¹, respectively. The regions of high- (up to 18.0 m s⁻¹ upstream) and low (down to 2.8 m s⁻¹ downstream)-air velocity zones for all the sprayers were accurately predicted. The simulation results showed that the Condor V sprayer had a highly disturbed vertical jet velocity profile, especially at higher heights. The Duoprop sprayer had high jet velocities at the two-fan positions and lower jet velocity in between the two fans. Within the canopy height the AirJet Quatt sprayer showed a more uniform distribution of air than the other two sprayers except the minor peaks at the fan positions. These situations were all confirmed by the measurements.     

Multi-spectral radiometry to estimate pasture quality components

Multi-spectral remote sensing of green vegetation provides an opportunity for assessing biophysical and biochemical properties. This technique could play a crucial role in pasture management by providing the means to evaluate pasture quality in situ. In this study, the potential of a 16-channel multi-spectral radiometer (MSR) for predicting pasture quality, crude protein (CP), acid detergent fibre (ADF), neutral detergent fibre (NDF), ash, dietary cation?Canion difference (DCAD), lignin, lipid, metabolisable energy (ME) and organic matter digestibility (OMD) was evaluated. In situ canopy spectral reflectance was acquired from mixed pastures, under commercial farm conditions in New Zealand. The multi-spectral data were evaluated by single wavelength, linear and non-linear renormalized difference vegetation index (RDVI), and stepwise multiple linear regression (SMLR) models. The selected non-linear, exponential fit, RDVI index models described (0.65????r ²????0.85) of the variation of pasture quality components (CP, DCAD, ME and OMD), while CP, ash, DCAD, lipid, ME and OMD were estimated with moderate accuracy (0.60????r ²????0.80) by the SMLR model. The remaining pasture quality components ADF, NDF and lignin were poorly explained (0.40????r ²????0.58) by the models. This experiment concluded that the MSR has potential to rapidly estimate pasture quality in the field using non-destructive sampling.     

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.