大豆叶面积变化对田间微气象条件及产量的影响

作者通过1977～1981年和1987～1989年,分别在绥化、哈尔滨和合江地区的田间试验和生产调查,发现大豆叶面积指数的大小可造成不同的田间微气象环境条件,叶面积指数6左右时,植株似倒非倒,株间采光量和CO_2分布较为合理,光合效率高,大豆单株生育良好,群体产量高;在生产上可依据当地土地条件、施肥水平、不同品种和不同种植方式用叶面积动态变化方程式计算出适宜的密度,以使大豆群体最大叶面积指数保持在6左右,创造较理想的田间微气象效应。     

基于机载LiDAR数据的玉米叶面积指数反演

以机载LiDAR离散点云数据为数据源,基于植被冠层孔隙率与叶面积指数的关系,提出一种反演大田玉米叶面积指数的方法。对反演LAI和实测LAI进行对比分析,结果表明:基于Axelsson改进的不规则三角格网加密方法可以将地面点和非地面点分开,结合高分辨率影像能够提取出玉米冠层点云;基于孔隙率反演LAI,尼尔逊参数的选择对结果影响很大,利用扫描天顶角模拟尼尔逊参数,LAI反演结果接近于真实情况。利用机载LiDAR点云数据能精确地反演大田玉米LAI,该研究方法适用于中等高度的农作物,可以扩展到甜菜、甘蔗等其他中等高度农作物。     

Deep neural network algorithm for estimating maize biomass based on simulated Sentinel 2A vegetation indices and leaf area index

Accurate estimation of biomass is necessary for evaluating crop growth and predicting crop yield.Biomass is also a key trait in increasing grain yield by crop breeding.The aims of this study were(i)to identify the best vegetation indices for estimating maize biomass,(ii)to investigate the relationship between biomass and leaf area index(LAI)at several growth stages,and(iii)to evaluate a biomass model using measured vegetation indices or simulated vegetation indices of Sentinel 2A and LAI using a deep neural network(DNN)algorithm.The results showed that biomass was associated with all vegetation indices.The three-band water index(TBWI)was the best vegetation index for estimating biomass and the corresponding R2,RMSE,and RRMSE were 0.76,2.84 t ha−1,and 38.22%respectively.LAI was highly correlated with biomass(R2=0.89,RMSE=2.27 t ha−1,and RRMSE=30.55%).Estimated biomass based on 15 hyperspectral vegetation indices was in a high agreement with measured biomass using the DNN algorithm(R2=0.83,RMSE=1.96 t ha−1,and RRMSE=26.43%).Biomass estimation accuracy was further increased when LAI was combined with the 15 vegetation indices(R2=0.91,RMSE=1.49 t ha−1,and RRMSE=20.05%).Relationships between the hyperspectral vegetation indices and biomass differed from relationships between simulated Sentinel 2A vegetation indices and biomass.Biomass estimation from the hyperspectral vegetation indices was more accurate than that from the simulated Sentinel 2A vegetation indices(R2=0.87,RMSE=1.84 t ha−1,and RRMSE=24.76%).The DNN algorithm was effective in improving the estimation accuracy of biomass.It provides a guideline for estimating biomass of maize using remote sensing technology and the DNN algorithm in this region.     

Simulation of transpiration, drainage, N uptake, nitrate leaching, and N uptake concentration in tomato grown in open substrate

Free-drainage or “open” substrate system used for vegetable production in greenhouses is associated with appreciable NO₃⁻ leaching losses and drainage volumes. Simulation models of crop N uptake, N leaching, water use and drainage of crops in these systems will be useful for crop and water resource management, and environmental assessment. This work (i) modified the TOMGRO model to simulate N uptake for tomato grown in greenhouses in SE Spain, (ii) modified the PrHo model to simulate transpiration of tomato grown in substrate and (iii) developed an aggregated model combining TOMGRO and PrHo to calculate N uptake concentrations and drainage NO₃⁻ concentration. The component models simulate NO₃⁻-N leached by subtracting simulated N uptake from measured applied N, and drainage by subtracting simulated transpiration from measured irrigation. Three tomato crops grown sequentially in free-draining rock wool in a plastic greenhouse were used for calibration and validation. Measured daily transpiration was determined by the water balance method from daily measurements of irrigation and drainage. Measured N uptake was determined by N balance, using data of volumes and of concentrations of NO₃⁻ and NH₄⁺ in applied nutrient solution and drainage. Accuracy of the two modified component models and aggregated model was assessed by comparing simulated to measured values using linear regression analysis, comparison of slope and intercept values of regression equations, and root mean squared error (RMSE) values. For the three crops, the modified TOMGRO provided accurate simulations of cumulative crop N uptake, (RMSE = 6.4, 1.9 and 2.6% of total N uptake) and NO₃⁻-N leached (RMSE = 11.0, 10.3, and 6.1% of total NO₃⁻-N leached). The modified PrHo provided accurate simulation of cumulative transpiration (RMSE = 4.3, 1.7 and 2.4% of total transpiration) and cumulative drainage (RMSE = 13.8, 6.9, 7.4% of total drainage). For the four cumulative parameters, slopes and intercepts of the linear regressions were mostly not statistically significant (P < 0.05) from one and zero, respectively, and coefficient of determination (r²) values were 0.96-0.98. Simulated values of total drainage volumes for the three crops were +21, +1 and −13% of measured total drainage volumes. The aggregated TOMGRO-PrHo model generally provided accurate simulation of crop N uptake concentration after 30-40 days of transplanting, with an average RMSE of approximately 2 mmol L⁻¹. Simulated values of average NO₃⁻ concentration in drainage, obtained with the aggregated model, were −7, +18 and +31% of measured values.     

不同灌水次数和施氮量对冬小麦群体动态和产量的影响

为了明确灌水次数和施氮量对高产冬小麦群体动态和产量的影响,分别于2006-2007、2007-2008年度在保定市和藁城市用当地冬小麦推广品种河农822和石新616研究了不同灌水次数(在保证底墒基础上全生育期灌0、1、2和3水,分别用W0、W1、W2和W3表示)和施氮量(0、112.5和225 kg/ha,分别用N0、N1和N2表示)对小麦群体动态和产量的影响.结果表明,2006-2007年度灌水次数和施氮量对小麦总茎(穗)数的影响较显著.W1、W2和W3的成穗数显著高于W0,W1、W2、W3之间差异不显著.3个施氮水平间各生育时期的总茎数均差异显著,且随施氮量增加而增大.大部分生育时期不同灌水次数的LAI差异不显著,而不同施氮量的LAI差异显著,N2的LAI显著高于N1和N0.灌水次数对穗数和千粒重影响显著,施氮量对穗数和穗粒数影响显著,以致灌水次数和施氮量对小麦产量的主效应均达到显著标准.4种灌水水平的产量以W3最高,W0最低;3个施氮量中N1产量最高,N0最低.灌水次数和施氮量对小麦产量和各产量构成因素的交互作用显著,W0和W1条件下产量随施氮量增加而提高,而W2和W3条件下N1产量最高,且与N0差异显著.2007-2008年度试验中,灌水次数和施氮量对各生育时期的总茎数、LAI和产量的影响均不显著.根据本研究结果可知,在河北平原地区常年降水(小麦全生育期100 mm左右)和中等肥力条件下全生育期灌溉3次,施氮量为N 112.5～225 kg/ha以及丰水降雨年份和较高肥力条件下全生育期灌溉1或2次,相应地施氮225或112.5 kg/ha,可以分别取得较理想的产量.     

基于无人机数码影像的冬小麦叶面积指数探测研究

叶面积指数(LAI)是评价作物长势的重要农学参数之一,利用遥感技术准确估测作物叶面积指数(LAI)对精准农业意义重大。目前,数码相机与无人机系统组成的高性价比遥感监测系统在农业研究中已取得一些成果,但利用无人机数码影像开展作物LAI估测研究还少有尝试。为论证利用无人机数码影像估测冬小麦LAI的可行性,本文以获取到的3个关键生育期(孕穗期、开花期和灌浆期)冬小麦无人机数码影像为数据源,利用数字图像转换原理构建出10种数字图像特征参数,并系统地分析了3个生育期内两个冬小麦品种在4种氮水平下的LAI与数字图像特征参数之间的关联性。结果表明,在LAI随生育期发生变化的同时,10种数字图像特征参数中R/(R+G+B)和本文提出的基于无人机数码影像红、绿、蓝通道DN值以及可见光大气阻抗植被指数(VARI)计算原理构建的数字图像特征参数UAV-based VARIRGB也有规律性变化,说明冬小麦的施氮差异不仅对LAI有影响,也对某些数字图像特征参数有一定影响;在不同条件(品种、氮营养水平以及生育期)下的数字图像特征参数与LAI的相关性分析中,R/(R+G+B)和UAV-based VARIRGB与LAI显著相关。进而,研究评价了R/(R+G+B)和UAV-based VARIRGB构建的LAI估测模型,最终确定UAV-based VARIRGB为估测冬小麦LAI的最佳参数指标。结果表明UAV-based VARIRGB指数模型估测的LAI与实测LAI拟合性较好(R2=0.71,RMSE=0.8,P0.01)。本研究证明将无人机数码影像应用于冬小麦LAI探测是可行的,这也为高性价比无人机遥感系统的精准农业应用增添了新成果和经验。     

两种植物冠层分析仪测量夏玉米LAI结果比较分析

植物冠层分析仪测量作物叶面积指数存在高估或低估现象,不同仪器的测量结果均与真实值存在差异,这种差异除了与仪器有关,还在不同的作物物候期表现出不同规律。本研究使用LAI-2000、Sunscan两种主流植物冠层分析仪测量夏玉米不同生育期LAI,并与LI-3000C叶面积仪实测结果比较。研究发现：当玉米LAI<1时,LAI-2000测量结果高于实际值,Sunscan测量结果与实际值无显著差异;12时,两种冠层分析仪测量结果均低于实际叶面积指数;不同LAI范围内,LAI-2000、Sunscan测量值与实际值的相关系数不同,最高可达0.9778和0.9637,通过建立测量值和实际值的经验关系模型,对仪器测量值进行校正可减少误差。     

地下水埋深对夏玉米根冠生长和耗水量的影响

利用排水式蒸渗仪,借助自制的Mariotte瓶装置,通过设置6种地下水埋深控制处理(地下水位埋深分别为0.2、0.4、0.6、0.8、1.01、.2m),探讨了不同地下水埋深对夏玉米的地上部分、根系生物量及耗水量的影响。结果表明,夏玉米株高和冠层叶面积随地下水埋深减小有增加的趋势,但是当地下水埋深减小到一定程度时就会抑制株高、叶面积,地下水埋深0.6m处理株高和叶面积系数(LAI)最大。各土层根系及其总生物量随地下水埋深增大而增大;地上部总生物量与地下部根生物量的比值随地下水埋深的增大呈明显减少的趋势。夏玉米全生育期和各阶段耗水量分别与地下水埋深呈较好的负相关关系,达到极显著水平(P<0.01);地下水补给量占耗水量的比例随地下水埋深的增大而逐步降低。研究结果可为江淮丘陵区夏玉米灌溉制度的制定及农田排水工程的设计提供参考依据。     

病害胁迫下玉米LAI遥感反演研究

为了在病害发生条件下进行玉米LAI的遥感估算,针对41个不同抗性的玉米自交系品种,通过人工接种方法,获得了不同病害严重程度(1～9级)的LAI数据,同时采集了地面高光谱和无人机多光谱数据,构建了K近邻算法、支持向量机、梯度提升分类树和决策分类树分类模型对病害进行分类,对玉米种质资源抗病性进行了划分。基于不同玉米病害胁迫程度分类结果,采用随机森林回归、梯度提升回归树、极端梯度增强算法、轻量梯度提升机4种机器学习模型对玉米LAI进行反演,讨论了不同模型在病害胁迫下的鲁棒性。研究结果表明,对不同生育期玉米病害程度进行划分,基于地面高光谱识别精度分别为84.72%(梯度提升分类树)、47.67%(支持向量机)、55.05%(K近邻算法)、83.02%(决策分类树)。基于病害分类结果,本文利用无人机多光谱数据估算了不同病情等级胁迫下的玉米LAI。构建了4种集成学习模型对不同病情等级的LAI进行估算,4个LAI反演模型的总体反演精度(rRMSE)分别为：19.11%(梯度提升回归树)、15.94%(轻量梯度提升机)、14.51%(随机森林回归)和15.45%(极端梯度增强算法)。其中极端梯度增强算...     

西安市生态环境植被信息的遥感反演

西安市自然生态环境问题突出,植被信息作为地表生态环境评价与监测的重要参数之一,对区域生态环境的监测及生态环境建设具有重要意义。为此,通过LandsatTM影像提取的植被指数与叶面积指数关系的相关分析,确定适合反映研究区植被信息的VI。结果证明,用NDVI作参考来监测植被状况是合理的,相关系数为0.7205。基于此进一步分析了植被指数与生态环境参数的关系,为西安市的生态环境监测提供依据。