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.     

精准施药中叶面积指数探测研究

精准施药技术的研究以提高农药的利用率为目的,是精准施药的发展方向。果树叶面积指数能为精准施药提供重要参考依据,通过叶面积指数及生物量探测可以进行大面积果树的长势监测并计算其药量的需求,进而对作物进行指导性施药。为此,结合国内外研究现状阐述了各类叶面积指数探测技术和计算方法,分析了精准施药技术中靶标探测的方法及叶面积指数探测在精准施药中的应用,并对精准变量施药中叶面积指数探测方法进行了展望。     

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。     

基于遥感技术的气象因素变化影响水稻单产定量研究

以黑龙江省五常市为研究区域，应用在水稻移栽期至成熟期选择的系列MODIS影像反演NDVI，通过NDVI间接反演LAI，并逐日拟合水稻全生育期的LAI，结合SIMRIW模型，计算研究区域2006年低温影响下的水稻单产。经演算，水稻单产值为5411．74kg／hm^2，是实际值的84％。研究结果表明应用高时间分辨率MODIS图像能够准确反演并拟合逐日LAI，可以为冷害影响下水稻单产的研究提供参考。     

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。     

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。     

基于夏玉米冠层内辐射分布的不同层叶面积指数模拟

为了模拟夏玉米冠层内各层叶面积指数垂直分布,光合有效辐射（photosynthetically active radiation, PAR）是研究作物群体光合作用和长势的重要特征参数,阐明冠层内PAR的垂直分布规律与冠层结构等参数之间的相关关系,可为遥感定量反演冠层结构参数提供模型基础。该文基于PAR在冠层内的辐射传输规律结合冠层结构模拟不同太阳高度角的PAR透过率垂直分布模型,并用地面冠层分析仪测量值进行验证,结果表明模型对封垄前玉米抽雄期冠层内PAR透过率垂直分布模拟精度较高。通过不同太阳高度角PAR透过率的垂直分布模型结合消光系数运用不同算法分别反演层叶面积指数（leaf area index, LAI）,并与不同高度层LAI实测值进行比较。结果显示：Bonhomme& Chartier算法反演不同高度层LAI精度较高,上层均方根误差（root mean square error,RMSE）为0.18,中层RMSE为0.55,下层RMSE为0.09。不同太阳高度角反演结果存在差异,30°和45°高度角均能较好地反演下层LAI,RMSE分别为0.11与0.09;30°高度角反演中层LAI精度较高,RMSE为0.30;45°高度角反演上层LAI精度较高,RMSE为0.18。结果表明基于不同太阳高度角构建的层LAI反演模型更适于实现夏玉米不同高度层LAI的遥感估算。该研究可为模拟垄行结构冠层内LAI垂直分布提供参考。     

Development of a ground truthing method for determination of rangeland biomass using canopy reflectance properties

Veld condition assessment and establishment of grazing capacity norms provide guidelines for the formulation of sustainable practices. However, conventional monitoring methods are becoming inadequate to meet future challenges, where quantification of spatial and temporal variation is required. This study proposes an on-site remote sensing method for monitoring above-ground biomass in rangelands. A preliminary model was formulated, based on simple regression relationships between canopy reflectance properties and aboveground biomass. This model was validated in semi-arid environments (Nama-karoo and Kalahari) within the framework of spatial and temporal experimental trials. Model accuracy was found to be primarily a function of canopy structure and vegetation composition where increases in dwarf shrub presence resulted in greater variations of both NDVI (Normalized Difference Vegetation Index) and LAI (Leaf Area Index) measurements. Temporal variation in model accuracy could also be observed which seemed to be associated with precipitation events. It was concluded that the proposed remote sensing method has potential as a ground truthing technique for determination of rangeland biomass. Indications are that this method is well suited for use in grass dominated veld types. With further refinement, implementation of this technique should also be possible in dwarf shrublands.     

积温对春玉米叶面积、生物量和产量的影响

进行积温对春玉米叶面积、生物量和产量影响试验,结果表明:积温与叶面积指数、生物量和产量之间存在密切的线性关系,积温每增加100℃.d,最大叶面积指数增加0.4,物量增加162 g/hm2,单产增加591 kg/hm2;积温每增加10%,玉米叶面积增加8%,最大生物量增加12.5%;气温升高1℃,玉米生物量增加243 g/m2,产量增加9.8%左右。反之,活动积温减少100℃.d左右,玉米单产减少8%～10%,将发生低温冷害。正常年份,当地适宜种中晚熟品种和正常偏晚播种。在水分条件比较适宜的条件下,气候变暖对提高玉米产量有利。     

小麦叶面积指数的遗传变异及其影响因素与产量的关系

以"小偃54×京411"的96个重组自交系(RILs)为试材,分析小麦叶面积指数(LAI)相关性状的遗传变异及其影响因素。结果表明,不同生育时期的LAI、MTA(平均叶倾角)、DIFN(无截取散射)均在RIL间存在极显著差异。LAI在RIL群体中呈连续的正态分布。LAI自抽穗期增大,至开花期达最大,灌浆后期降低。不同生育时期的LAI均与群体总茎数、株高和倒三叶叶长呈极显著正相关。灌浆后期的LAI与生物量和产量呈极显著正相关。当控制总茎数和株高不变时,LAI主要与顶三叶的叶面积和叶长呈正相关,其相关系数在开花期最大,灌浆后期降低。对抽穗期LAI>4.0的RIL系而言,该时期LAI与生物量和产量相关性不显著,但灌浆后期的群体平均叶倾角与生物量和产量呈显著正相关。对产量高于京411且株高小于80cm的8个RIL系的分析表明,旗叶与倒二叶叶长比与生物量和产量呈正相关。