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。     

高光谱遥感森林叶面积指数估测研究现状

文中简要概述了叶面积指数和高光谱遥感的概念, 分析比较了高光谱遥感与传统的宽波段遥感估测森林叶面积指数的优势, 以及利用高光谱遥感数据估测森林叶面积指数的常用方法, 并对目前高光谱遥感估测森林叶面积指数存在的问题行了讨论。     

基于MODIS反演逐日LAI及SIMRIW模型的冷害对水稻单产的影响研究

该文旨在将遥感技术与水稻作物模型SIMRIW相结合,以黑龙江省五常市为例,研究冷害对水稻单产的影响。通过在水稻移栽期至成熟期选取的系列MODIS图像计算NDVI,进而反演水稻全生育期逐日LAI,结合SIMRIW模型计算水稻单产。计算结果表明2006年五常市水稻单产值为5411.74 kg/hm^2,为实际值的83%;而在4种用于LAI反演的统计模型中,抛物线模型精度最好;整体拟合法在逐日LAI计算中要优于分段拟合法。这说明应用MODIS能够准确反演并拟合逐日LAI,它可为冷害影响水稻单产的评价研究提供参考。     

不同季节放牧对矮嵩草草甸植物叶面积指数的影响

采用样方法对青藏高原高寒矮嵩草草甸冬春季放牧、夏秋季放牧和不放牧样地各类植物叶面积指数、相对生长速率、地上活植物量以及叶茎比进行了调查分析,以研究不同季节放牧对矮嵩草草甸的影响。结果表明,7~8月,地上活植物量都是先增加后降低,7月24日达到峰值,其中夏季放牧地上活植物量最大,322.7 g/m2。不放牧对其叶面积指数的影响不大,夏季放牧其叶面积指数不断增大,8月8日达到最大值3.9;冬季放牧其叶面积指数先降低后增加,7月24日降到最小值2.9;地上活生物量与叶面积指数之间呈正相关。3种处理下矮嵩草草甸植物1个月内相对生长速率变化不同,7月9日~7月24日,群落地上植物量的净积累为正增长过程(RGR0),7月24日~8月9日呈负增长(RGR0),植物量下降。冬春季放牧下矮嵩草草甸各类植物的叶茎比不断增加,8月8日达最大值6;夏秋季放牧先增加后降低,7月24日达最大值6.9;不放牧先降低后增加,7月24日降到最小值4.3,8月8日达到最大值6.6。     

咸水畦灌农田土壤水热盐动态及油葵生长的试验与模拟

为探究中国西北旱区咸水畦灌条件下农田土壤水热盐动态及其对作物生长的影响,采用大田试验和WASH-C模型(Layered Soil Water-Solute-Heat Transport and Crop Growth Model,土壤水热盐迁移和作物生长耦合的模拟模型)模拟相结合的方法,分析油葵全生育期内不同灌水量和矿...     

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。