基于无人机遥感的果园冠层氮素估算及空间分析

为快速准确地获取植株冠层氮素含量及空间分布特征，对大尺度的果园进行精准动态的管理，以宽行窄株小冠模式、宽行窄株篱壁模式和传统栽培模式3种栽培模式的120棵柑橘树为研究对象，通过测定冠层氮素含量并提取无人机遥感影像多光谱数据中的纹理指数和植被指数，运用随机森林算法（RF）建立基于植被指数、纹理指数以及融合植被指数和纹理指数的柑橘冠层氮素反演模型，并比较融合植被指数和纹理指数的支持向量机（SVM）、BP神经网络算法（BP）和RF的模型反演精度。结果显示：在随机森林算法中，融合植被指数和纹理指数比单独的植被指数或纹理指数更能准确预测柑橘冠层氮素含量；植被指数训练集R²为0.710，测试集R²为0.430；纹理指数训练集R²为0.761，测试集R²为0.349；融合植被指数和纹理指数训练集R²为0.775，测试集R²为0.533。融合植被指数和纹理指数在SVM算法训练集R²为0.511，测试集R²为0.371；BP神经网络训练集R²为0.651，测试集R²为0.204。用融合植被指数和纹理指数的RF模型对3种栽培模式的柑橘园进行氮素反演，得到宽行窄株小冠模式的柑橘冠层平均氮素含量最高，其次为宽行窄株篱壁模式，传统栽培模式最低，氮素含量均值分别为31.33、30.20和27.82 mg/g。结合无人机遥感与融合植被指数和纹理指数的随机森林算法能够有效预测柑橘冠层氮素含量，可为大尺度柑橘果园定量施肥提供参考。     

新疆喀纳斯国家自然保护区植被叶面积指数观测与遥感估算

叶面积指数(Leaf Area Index,LAI)是重要的植被结构参数,调控着植被与大气之间的物质与能量交换,在生态环境脆弱的我国西北部开展植被LAI的研究对阐明该地区植被对气候变化和人类活动的响应特征具有重要的科学意义。利用LAI-2200和TRAC仪器观测了新疆喀纳斯国家级自然保护区森林和草地的有效叶面积指数(LAI_e)和真实LAI,构建了其遥感估算模型,生成了研究区LAI_e和LAI的空间分布图。在此基础上,分析了LAI随地形因子(海拔、坡度、坡向)的变化特征,探讨了将其应用于估算研究区森林生物量密度的可行性,并评估了研究区MODIS LAI产品的精度。结果表明:研究区阔叶林、针阔混交林、针叶林、草地LAI_e的平均值分别为4.40、3.18、2.57、1.76,LAI的平均值分别为4.76、3.93、3.27、2.30。LAI_e和LAI的高值主要集中分布在湖泊和河流附近;植被LAI随海拔、坡度和坡向的变化表现出明显的垂直地带性的特点。LAI随海拔和坡度的增加呈现先增加后减小的变化趋势,坡向对针叶林和草地LAI的影响明显,但对阔叶林和针阔混交林LAI的影响较弱;森林生物量密度(BD)随LAI增加而线性增加(BD=44.396LAI-25.946,R²=0.83),研究区森林生物量密度平均值为120.3 t/hm²,估算的总生物量为5.0×10⁶t;MODIS LAI产品与利用TM数据生成的LAI之间具有一定的相似性(森林R²=0.42,草地R²=0.53),但森林和草地的MODIS LAI产品分别比利用TM数据生成的LAI偏低16.5%和24.4%。     

白粉病胁迫下小麦叶片光谱响应与智能分类识别

目的 开展基于高光谱技术的白粉病胁迫下田间小麦光谱的响应研究,实现小麦白粉病感染等级的快速确定。方法 采用光纤光谱仪配合积分球和叶片夹采集大田活体小麦叶片可见-近红外光谱;通过光谱数据拟合得到的SF-SPAD (Spectrum fitting SPAD)值来反映叶绿素含量,对叶片感染白粉病进行初步判定;使用PROSPECT模型进行光谱敏感度分析确定敏感波段;结合主成分分析(Principal component analysis, PCA)降维和支持向量机(Support vector machine,SVM)建模,实现对光谱数据的二分类;根据二分类模型判断的病点百分比对小麦病虫害感染程度进行分级。结果 SF-SPAD值随自下而上的叶序的增大而逐渐上升;SF-SPAD值≤0.90的全是病点,≥1.05的全是好点。光谱敏感度分析确定了敏感波段为可见光波段440~500和540~780 nm,降低了数据维度。确定了感染等级(R)与病点百分比(%)的关系为R₁：0~30%、R₂：30%~50%、R₃：50%~70%、R₄：70%~100%。本研究所建模型适用的检测株数最少为20株。结论 结合SF-SPAD值和光谱PCA-SVM二分类建立的监测模型可以准确、快速地判定小麦白粉病感染与否及感染等级,同时可以降低采样数量、减少地面检测工作量、提高检测效率,是一项实用性强、简单、易推广的智能化监测技术。     

侧向风对航空植保无人机平面扇形喷头雾滴飘移的影响

目的 侧向风是影响植保无人机航空喷施雾滴飘移和作业效果的主要因素。探究航空植保喷施过程中侧向风对雾滴沉积和飘移的影响,为植保无人机航空喷施作业参数的选择和作业关键部件的改进提供数据支持和理论指导。方法 以常用平面扇形喷头Lechler系列的LU 120-015和LU 120-03标准压力喷头为研究对象,基于计算流体力学离散相模型的粒子跟踪技术,在适宜的边界条件下对喷施作业过程中风洞内雾滴流场和农药喷洒离散相进行模拟试验;通过仿真模拟对平面扇形喷头喷施的雾滴沉积和飘移分布情况进行可视化分析,探究雾滴粒子在不同侧风风速条件下的飘移特性;在农业航空专用风洞中,采用近似条件对雾滴的沉积飘移特性进行试验验证和分析。结果 仿真模拟结果表明,随着侧向风速的增加,离散相雾滴粒子飘移程度越严重,雾滴水平飘移越明显。随着侧向风速的增加,模拟离散相雾滴粒子的准确沉积率(R_a)呈指数下降,由14.11%下降到0.66%;水平飘移率(R_h)呈线性增加,由14.25%增加到60.58%。风洞试验结果表明,在侧风风速分别为1、3和6 m/s的条件下,雾滴的R_h分别为0.4%、48.1%和75.1%,且雾滴在风洞内部会随着侧风风速的增加发生一定程度的卷扬现象。仿真模拟与风洞测试试验的R_h具有显著相关性(R²=0.963,P<0.05)。结论 仿真模拟对航空喷施条件下的雾滴飘移具有较好的预测效果;采用仿真模拟辅助风洞试验测试的方法,可以比较准确地得出航空植保无人机作业中常用平面扇形喷头的雾滴沉积与飘移情况。     

协同冠层SIF和PRI光谱指数的构建及其在小麦条锈病监测中的应用

【目的】利用反射率光谱在作物生物物理方面的优势和日光诱导叶绿素荧光(solar-induced chlorophyll fluorescence,SIF)、光化学反射率指数(photochemical reflectance index,PRI)在光合生理方面的优势,构建协同冠层SIF和PRI光谱指数(synergistic spectral index of SIF and PRI,SISP),旨在提高作物病害遥感探测精度。【方法】基于3FLD(three bands fraunhofer line discrimination)算法,估测小麦条锈病在不同病情严重度下的单波段SIF强度,利用对作物冠层几何结构敏感的归一化植被指数(normalized difference vegetation index,NDVI)和重归一化植被指数(re-normalized vegetation index,RDVI)对SIF和PRI进行处理,再利用处理后的SIF和PRI数据构建SISP指数,通过建立传统的光谱指数和SIF、PRI及其组合对小麦条锈病的遥感监测模型,以病情指数(disease index,DI)实测值与预测值之间的决定系数(R²)、均方根误差(RMSE)和相对分析误差(RPD)评价模型精度,进而与SISP指数建立的模型进行比较,分析SISP指数对作物病害遥感监测的有效性。【结果】（1）综合利用SIF和PRI数据能够提高对小麦条锈病的遥感探测精度,3组验证样本数据集中,以PRI和SIF的简单组合PRI+SIF为自变量构建的小麦条锈病监测模型,预测DI值与实测DI值间的R²比单一PRI和SIF至少提高14.0%和1.7%,RMSE至少降低7.1%和3.7%。（2）利用反射率光谱指数NDVI和RDVI处理后的SIF和PRI构建的SISP指数,对小麦条锈病DI的预测精度优于直接利用PRI和SIF组合的各种指数,验证样本数据集中预测DI值与实测DI值间的R²至少提高3.7%,RMSE至少降低9%。（3）以SISP和反射率光谱指数为自变量构建的小麦条锈病多元线性回归(multiple linear regression,MLR)和径向基神经网络(radial basis function neural network,RBFN)模型的精度,高于仅利用反射率光谱指数构建的模型精度,其预测DI值与实测DI值间的R²分别较反射率光谱指数提高13.42%和5.72%,RMSE分别减少29.93%和19.24%,RPD分别提高44.53%和29.80%。【结论】利用NDVI和RDVI处理后的SIF和PRI构建SISP指数,能够减弱作物群体生物量对冠层SIF和PRI信号的影响,提高小麦条锈病的遥感监测精度。     

玉米幼苗根系对Cr（Ⅲ）的吸收动力学特征及途径探讨

为探讨玉米幼苗对Cr（Ⅲ）的吸收途径,通过水培试验方法,研究了不同Cr（Ⅲ）处理浓度（0~20 mg·L^-1）对玉米幼苗Cr（Ⅲ）的吸收动力学特征的影响,以及ATP酶抑制剂、离子通道抑制剂、Fe（Ⅲ）对玉米幼苗根系Cr（Ⅲ）吸收的影响。结果表明：玉米幼苗根系吸收Cr（Ⅲ）的最大吸收速率（V_max）为121.4 mg·kg^-1·d^-1,显著高于地上部的16.3 mg·kg^-1·d^-1（P<0.05）;根系吸收Cr（Ⅲ）的离子载体与离子的亲和力（K_m）为12.2 mg·L^-1,显著低于地上部分的180.8 mg·L^-1（P<0.05）。ATP酶抑制剂Na3VO4显著抑制了玉米幼苗根系对Cr（Ⅲ）的吸收（P<0.05）。Ca²⁺通道抑制剂LaC_l3和K⁺通道抑制剂C₈H₂₀CIN均不抑制玉米幼苗根系对Cr（Ⅲ）的吸收（P>0.05）。与正常营养液相比,缺Fe（Ⅲ）处理下的玉米幼苗根系吸收Cr（Ⅲ）的V_max增加了20.0%,K_m数值仅减少了5.0%,但Fe（Ⅲ）处理下的玉米幼苗根系吸收Cr（Ⅲ）的V_max减少了46.0%,K_m数值减少了56.0%。研究表明,玉米幼苗吸收的Cr（Ⅲ）主要富集在玉米幼苗的根部,只有很少被转移到地上部分;玉米幼苗根系对Cr（Ⅲ）的吸收存在主动吸收的过程,进入细胞膜的途径与Ca²⁺和K⁺通道无关,Fe（Ⅲ）抑制了玉米幼苗根系对Cr（Ⅲ）的吸收。     

关中地区李树遥感辨识的最佳时相与方法

[目的] 探寻李树遥感辨识的最佳时相与方法，为关中地区以及其他果区的李树遥感监测提供理论与方法支撑。[方法] 文章以关中地区为研究区域，基于16种地物样地的感兴趣区数据，采用反射光谱及其差分序列对比与分析、光谱距离法、图像增强处理与分析法、图像差值与比值法、光谱指数法、光谱指数变化分析法和辨识方法优化组合7类方法，探究李树遥感识别并对辨识精度进行了验证。[结果] （1）李树遥感辨识的最佳时相为盛花期；（2）R₆₆₀/R₅₅₅阈值法对盛花期的李树具有较强的辨识效能；（3）两指数（NDVI_3-19与R₄₈₅+R₅₅₅）阈值联用法可以较高精度将盛花期的李树与同时期的梨树、冬小麦、冬油菜、撂荒地予以区分，但是该方法难以将李树与其他10种果树精确区分；（4）三指数（R₆₆₀/R₅₅₅、NDVI_3-19与R₄₈₅+R₅₅₅）阈值联用法可将盛花期的李树与同时期除撂荒地以外的其他地物予以精确区分，但是该方法对李树与撂荒地的区分精度依然不够理想；（5）NDVI_10-19阈值法可将10月中旬的李树与撂荒地精确区分；（6）四指数（R₆₆₀/R₅₅₅、NDVI_10-19、R₄₈₅+R₅₅₅与NDVI_3-19）阈值联用法可高精度识别当年的李树，李树类的分类精度可达95.49%，非李地物类的分类精度可达96.02%，总体分类精度可达95.92%。[结论] 开展李树遥感监测时，融合李树盛花期与10月中旬两期影像，采用四指数阈值联用方法可获得较高的监测精度。     

粳稻冠层叶绿素含量PSO-ELM高光谱遥感反演估算

目的 叶绿素含量是表征粳稻生长状态的重要指示信息，利用无人机高光谱遥感技术及时获取区域尺度的粳稻叶绿素含量。方法 以2016—2017年沈阳农业大学辽中水稻实验站粳稻无人机遥感试验数据为基础，利用连续投影算法(SPA)进行有效波段的提取，提取的特征波段分别为410、481、533、702和798 nm。将提取出的特征波段作为输入，利用极限学习机(ELM)和粒子群优化的极限学习机(PSO-ELM)分别建立粳稻冠层叶绿素含量反演模型。在PSO-ELM模型中，针对PSO算法的种群规模(p)、惯性权重(w)、学习因子(C₁、C₂)、速度位置相关系数(m)这5个参数进行了优化。结果 确定了最优参数：p为80，w为0.9~0.3线性递减，C₁和C₂分别为2.80和1.10，m为0.60。利用优化后的ELM和PSO-ELM所建立的粳稻冠层叶绿素含量模型的决定系数分别为0.734和0.887，均方根误差分别为1.824和0.783。结论 利用优化后的PSO-ELM建立的粳稻叶绿素含量反演模型精度要明显高于单纯利用ELM建立的反演模型，前者具有较好的粳稻叶绿素含量反演能力。本研究为东北粳稻叶绿素含量反演无人机遥感诊断提供了数据支撑和应用基础。     

西北旱区不同覆膜和灌溉水平下的玉米冠层氮含量垂直分布及高光谱反演

为探究不同覆膜和灌溉水平下玉米叶片氮含量垂直分布特征及其遥感反演规律,2020年在甘肃省武威绿洲农业高效用水国家野外科学观测研究站进行大田试验,设置3种灌水量水平(春玉米灌溉需水量的100%(W₁₀₀)、70%(W₇₀)和40%(W₄₀))和3种覆膜处理(不覆膜(M₀)、普通塑料膜(M₁)和生物可降解膜(M₂)),测定春玉米在不同灌水量和覆膜条件下叶片氮含量垂直分布、冠层反射特征和反射率与叶片氮含量等指标,并采用随机森林法构建氮含量估测模型分析垂直分布的叶片氮含量。结果表明,相同灌水处理的玉米冠层叶片中氮含量由高到低为M₀>M₂>M₁,M₀比M₂的上、中、下部位叶片氮含量分别增加6.78%、5.11%、2.55%,M₂比M₁的上、中、下部位叶片氮含量分别增加7.14%、5.24%、5.39%。相同覆膜条件下玉米冠层叶片氮含量由高到低为W₁₀₀>W₇₀>W₄₀,W₁₀₀比W₇₀的上、中、下部位叶片氮含量分别增加6.84%、6.23%、7.74%,W₇₀比W₄₀的上、中、下部位叶片氮含量分别增加4.41%、3.32%、9.49%。在M₀W₁₀₀处理中,玉米冠层叶片氮含量从上到下依次减小,上部比中部叶片氮含量增加7.44%,中部比下部叶片氮含量增加7.60%。在相同覆膜条件下,在可见光波段范围内,冠层反射率随着灌水量的增加而降低;在近红外波段范围内,冠层反射率随着灌水量的增加而增加。综上,基于随机森林的春玉米不同垂直部位叶片中氮含量估算模型均与实测结果相吻合(验证的R²>0.5),上部叶片中氮含量估算精度最高(R²为0.63,RMSE为1.66 g/kg,RPD为1.57),其次为中部叶片(R²为0.73,RMSE为1.66 g/kg,RPD为1.30),精度最低的下部叶片(R²为0.00)。     

陕北山地苹果园土壤饱和导水率和植物导水率特征

为揭示陕北山地果园水分分布及运移规律，以陕北山地苹果园为研究对象，探究不同生育期土壤饱和导水率(K_s)和植物导水率(K_p)特征，分析土壤理化性质对其的影响。结果表明：①土壤饱和导水率在成熟期最高，为0.86 mm/min；幼果期、着色期和开花期土壤导水率次之，分别为0.77、0.72、0.65 mm/min；果实膨大期土壤饱和导水率最低为0.47 mm/min。②植物导水率在不同生育期表现为开花期>着色期>幼果期>成熟期>果实膨大期，它们的植物导水率分别为8.25×10^-5、6.12×10^-5、4.25×10^-5、3.38×10^-5、 3.34×10^-5 kg/（s·MPa）。③多元逐步回归分析得到K_s与K_p的传递函数，并对其进行检验发现预测值与实测值相差不大，R均在0.9左右，能较为准确地预测K_s与K_p，建立的函数模型状况良好；④通径分析可知有机质含量对土壤饱和导水率和植物导水率都有直接正效应，粉粒含量对土壤饱和导水率有间接正效应。     

Site-specific early season potato yield forecast by neural network in Eastern Canada

Deterministic potato (Solanum tuberosum L.) growth models hardly rely on driving seasonal field variables that directly characterize spatial variation of plant growth. For example, the SUBSTOR model computes the leaf area index (LAI) as an auxiliary variable from meteorological conditions and soil properties. Empirical models may account for seasonal LAI functions and accurately predict potato yield. The objective was to evaluate multiple linear regression (MLR) and neural networks (NN) as predictive models of potato yield. Using data from several replicated on-farm experiments conducted over 3 years, model performance was evaluated for their capacity to forecast tuber yields 9, 10 and 11 weeks before harvest compared to SUBSTOR. A 3-input NN using LAI functions and cumulative rainfall yielded the most accurate estimations and forecasts of tuber yields. This NN showed that tuber yield of contrasting zones was mostly a function of meteorological conditions prevailing during the first 5–8 weeks after planting. Subsequent development of tubers was essentially controlled by biomass allocation to tubers. The NN models were more coherent than MLR and SUBSTOR for two reasons: (1) the use of seasonal LAI directly as input rather than computed as an auxiliary variable and (2) the non-linearity of the modeling process resulting in more accurate estimation of the temporal discontinuities of potato tuber growth. This model showed potential for application in precision agriculture by accounting for temporal and spatial real-time climatic and crop data.     

A neural network experiment on the site-specific simulation of potato tuber growth in Eastern Canada

The objective of this work was to optimize a neural network (NN) for modelling potato tuber growth and its in-field variations in eastern Canada. In addition to climatic inputs, the cumulative and maximal leaf area index (LAI) were incorporated to account for in-field scale variability. Soil and genetic parameters were assumed to be integrated in LAI as suggested by earlier work. Each input and combination of inputs was evaluated from the changes they induced in MAE (mean absolute error) and RMSE (root mean square error). Results using data from several replicated on-farm experiments between 2005 and 2008 suggest that a NN model using cumulative solar radiation, cumulative rainfall and cumulative LAI can adequately model site-specific tuber growth. The MAE of the retained model was 209 kg DM ha⁻¹, which represents less than 4% of the mean final tuber yield for the 3 years of the study. Non-linear effects of explicative variables on tuber yield were attested by comparing the results of the NN simulations to those of a multiple linear regression (MLR). The failure of MLR to simulate temporal discontinuities in tuber growth supports the use of a non-linear approach such as a NN to model tuber growth.     

基于无人机多光谱遥感的冬小麦叶面积指数反演

以获取的冬小麦无人机多光谱影像为数据源,充分利用多光谱传感器的红边通道对传统植被指数进行改进,通过灰色关联度分析后基于多个植被指数建模的方法对冬小麦的叶面积指数(leaf area index,LAI)进行反演精度对比。结果显示:使用基于多植被指数的随机森林(RF)比赤池信息量准则-偏最小二乘法(AIC-PLS)反演精度高。得到的LAI反演值和真实值之间的R~2=0.822,RMSE=1.218。研究证明通过随机森林预测具有更好的拟合效果,对冬小麦的LAI反演有较好的适用性。     

基于双波段光谱仪CGMD-302的小麦叶面积指数和叶干重监测

为探究双波段光谱仪CGMD-302在监测小麦长势上的可靠性与精准性,同时使用高光谱仪UniSpec SC与双波段光谱仪CGMD-302测试各生育时期小麦冠层信息,并定量分析了植被指数NDVI、RVI、DVI与叶面积指数和叶片干重之间的线性关系。结果表明,基于相同波段反射率计算出的高光谱仪植被指数和双波段光谱仪植被指数均能较好监测小麦群体长势。在CGMD-302监测的叶面积指数模型中,拟合方程的决定系数(R~2)均高于0.89,用以检验模型的均方根误差(RMSE)和相对误差(RE)分别小于0.792和0.225;叶片干重模型中,决定系数(R2)均高于0.85,用以检验模型的均方根误差(RMSE)和相对误差(RE)分别小于440kg/hm~2和0.239。通过分析发现,施氮270kg/hm~2既能保证产量又能兼顾品质,可作为适宜施氮量。适宜施氮量下,拔节期和孕穗期小麦适宜叶面积指数分别为:3.65±0.09和5.95±0.32;适宜叶干重分别为:(1 554±168)和(2 231±130)kg/hm~2。结合CGMD-302监测模型可推算出拔节期和孕穗期适宜冠层群体的植被指数区间并应用于冠层群体诊断。     

A double-layer model for improving the estimation of wheat canopy nitrogen content from unmanned aerial vehicle multispectral imagery

The accurate and rapid estimation of canopy nitrogen content (CNC) in crops is the key to optimizing in-season nitrogen fertilizer application in precision agriculture. However, the determination of CNC from field sampling data for leaf area index (LAI), canopy photosynthetic pigments (CPP; including chlorophyll a, chlorophyll b and carotenoids) and leaf nitrogen concentration (LNC) can be time-consuming and costly. Here we evaluated the use of high-precision unmanned aerial vehicle (UAV) multispectral imagery for estimating the LAI, CPP and CNC of winter wheat over the whole growth period. A total of 23 spectral features (SFs; five original spectrum bands, 17 vegetation indices and the gray scale of the RGB image) and eight texture features (TFs; contrast, entropy, variance, mean, homogeneity, dissimilarity, second moment, and correlation) were selected as inputs for the models. Six machine learning methods, i.e., multiple stepwise regression (MSR), support vector regression (SVR), gradient boosting decision tree (GBDT), Gaussian process regression (GPR), back propagation neural network (BPNN) and radial basis function neural network (RBFNN), were compared for the retrieval of winter wheat LAI, CPP and CNC values, and a double-layer model was proposed for estimating CNC based on LAI and CPP. The results showed that the inversion of winter wheat LAI, CPP and CNC by the combination of SFs+TFs greatly improved the estimation accuracy compared with that by using only the SFs. The RBFNN and BPNN models outperformed the other machine learning models in estimating winter wheat LAI, CPP and CNC. The proposed double-layer models (R²=0.67–0.89, RMSE=13.63–23.71 mg g^–1, MAE=10.75–17.59 mg g^–1) performed better than the direct inversion models (R²=0.61–0.80, RMSE=18.01–25.12 mg g^–1, MAE=12.96–18.88 mg g^–1) in estimating winter wheat CNC. The best winter wheat CNC accuracy was obtained by the double-layer RBFNN model with SFs+TFs as inputs (R²=0.89, RMSE=13.63 mg g^–1, MAE=10.75 mg g^–1). The results of this study can provide guidance for the accurate and rapid determination of winter wheat canopy nitrogen content in the field.     

黄腐酸对秋马铃薯农艺性状及产量的影响(英文)

[目的]研究黄腐酸对秋马铃薯农艺性状及产量的影响。[方法]分析黄腐酸对秋马铃薯生育期、植株形态、块茎、净光合速率、叶绿素含量、叶面积指数(LAI)和产量的影响。[结果]施用黄腐酸可延长秋马铃薯生育期,增加植株株高、茎粗、叶片叶绿素含量和LAI,提高叶片有效净光合速率、单株薯块数、单株薯块重和商品薯率,从而提高产量。施用黄腐酸后可延长秋马铃薯生育期1~3 d,显著或极显著提高植株株高、叶片净光合速率、单株块茎重和产量。在复合肥减半的情况下施用黄腐酸与复合肥常量施用下的出苗期和成熟期一致、生育期相当、净光合速率无差异性,特别是固体肥和液体肥配施后产量无差异性。[结论]施用黄腐酸后在产量不变的情况下可减少化学肥料的投入,降低化学肥料的面源污染。     

Leaf area index based nitrogen diagnosis in irrigated lowland rice

Leaf area index(LAI)is used for crop growth monitoring in agronomic research,and is promising to diagnose the nitrogen(N)status of crops.This study was conducted to develop appropriate LAI-based N diagnostic models in irrigated lowland rice.Four field experiments were carried out in Jiangsu Province of East China from 2009 to 2014.Different N application rates and plant densities were used to generate contrasting conditions of N availability or population densities in rice.LAI was determined by LI-3000,and estimated indirectly by LAI-2000 during vegetative growth period.Group and individual plant characters(e.g.,tiller number(TN)and plant height(H))were investigated simultaneously.Two N indicators of plant N accumulation(NA)and N nutrition index(NNI)were measured as well.A calibration equation(LAI=1.7787LAI_(2000)–0.8816,R~2=0.870~(**))was developed for LAI-2000.The linear regression analysis showed a significant relationship between NA and actual LAI(R~2=0.863~(**)).For the NNI,the relative LAI(R~2=0.808~(**))was a relatively unbiased variable in the regression than the LAI(R~2=0.33~(**)).The results were used to formulate two LAI-based N diagnostic models for irrigated lowland rice(NA=29.778LAI–5.9397;NNI=0.7705RLAI+0.2764).Finally,a simple LAI deterministic model was developed to estimate the actual LAI using the characters of TN and H(LAI=–0.3375(TH×H×0.01)~2+3.665(TH×H×0.01)–1.8249,R~2=0.875~(**)).With these models,the N status of rice can be diagnosed conveniently in the field.     

融合无人机光谱信息与纹理信息的冬小麦生物量估测

【目的】生物量是表征植被生命活动的重要参数,对植被长势监测、产量预测有重要意义。以无人机为平台的高光谱遥感技术,具有机动灵活、成本低、空间覆盖广的优势,能够及时准确地估测植被生物量,已成为遥感估算研究的热点之一。由于光谱特征反演生物量存在饱和问题,因此,本研究尝试结合纹理特征与植被指数构建一种"图-谱"融合指标,探究"图-谱"融合指标的抗饱和能力及生物量估测能力。【方法】首先,利用无人机高光谱影像,提取其光谱信息和纹理信息,分别基于植被指数和纹理特征构建生物量模型。其次,针对光谱特征存在的饱和问题,将植被指数与对生物量敏感的纹理指标相乘或相除两种形式构建"图-谱"融合指标,分析"图-谱"融合指标的饱和性,并基于"图-谱"融合指标构建生物量估算模型。最后,对比不同指标构建的生物量模型的估测效果,来分析"图-谱"融合指标估测生物量的能力。【结果】(1)植被指数多在LAI=5时出现饱和现象,而"图-谱"融合指标VI×sm658,VI/ent658,VI/dis658,VI/con658,VI/dis514,VI/con514,VI/var514,VI×con802,VI×dis802均在LAI5时才出现饱和现象,相比之下,这些"图-谱"融合指标一定程度上改善了饱和问题;(2)与植被指数相比(除了GNDVI、NDVI之外),抗饱和能力提高的"图-谱"融合指标VI×sm658、VI/ent658、VI/dis658、VI/con658、VI/dis514、VI/con514、VI/var514、VI×con802、VI×dis802,其与生物量的相关性也相对提高,所构建的生物量模型精度较高(R2=0.81,RMSE=826.02 kg·hm-2)。(3)对比单一植被指数、纹理特征,将纹理特征与光谱特征相结合的"图-谱"融合指标估算小麦生物量的能力相对最强,模型精度明显高于单一植被指数(R2=0.69)和单一纹理特征(R2=0.71)构建的生物量模型。【结论】"图-谱"融合指标的抗饱和能力明显提高,其构建的生物量模型精度也有效提高,实现了结合光谱信息和纹理信息的冬小麦生物量遥感估测,为生物量定量反演提供一种新思路。     

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.