基于无人机遥感影像的玉米冠层温度提取及作物水分胁迫监测

针对当前无人机热红外遥感提取冠层温度不准确、监测作物水分胁迫状况精度不高的问题,该研究以不同水分处理的拔节期夏玉米为研究对象,利用无人机获取试验区域热红外和可见光图像资料,分别采用Otsu算法、EXG-Kmeans算法和Otsu-EXG-Kmeans算法获取冠层区域图像,并对提取结果进行精度评价,而后采用最优算法求得对应作物水分胁迫指数（Crop Water Stress Index,CWSI）,通过分析CWSI同土壤含水率相关关系以及CWSI日平均变化趋势来监测玉米水分亏缺状况。结果表明：1）相比于其他方法,Otsu-EXG-Kmeans算法对冠层温度提取精度更高（用户精度为95.9%）,提取的冠层温度更接近实测温度（r=0.788）,可以准确获取图像冠层温度。2）相比于冠层温度,CWSI与土壤含水率的相关性更高（r= -0.738）,CWSI日平均变化趋势更符合实际情况,可更加精确地监测玉米缺水状况。该研究为无人机遥感精准监测作物水分胁迫状况提供参考。     

基于冠层光谱植被指数的冬小麦作物系数估算

目前针对局地气候条件下某一作物类型的作物系数及其年际变化已开展了较多分析,但适于区域尺度运用的作物系数估算方法的研究还比较缺乏,这是将FAO 56作物系数法成功应用于区域作物实际蒸散量估算的关键环节。该文基于2008-2009和2009-2010年度2个冬小麦生长季的大田试验数据,研究了作物系数（Kc）、基本作物系数（Kcb）与8种常用冠层光谱植被指数（VIs）的相关关系以及水分和氮素胁迫对其的影响,分析了基于VIs估算作物Kc、Kcb的可行性,并对其估算精度进行了验证。结果表明,高氮水平下Kcb较大而土壤蒸发系数（Ke）较小,低氮水平下Kcb较小而Ke较大,不同施氮水平下Kc无明显规律性差异。冬小麦Kc与VIs相关性较弱（决定系数R2＝0.094～0.150,p<0.01,n＝195）,而Kcb与VIs则具有很强的相关性（决定系数R2＝0.511～0.685, p<0.01,n＝195）;施氮水平不影响 Kcb-VIs 关系,而不足以使冠层光谱出现明显表征的水分胁迫可使 Kcb-VIs相关关系减弱。利用VIs估算的冬小麦实际生长条件下的Kcb值与FAO 56确定的Kcb值均具有很好的线性回归关系（R2＝0.765～0.864,n＝150）,其中增强型植被指数（EVI）的估算精度最好。但在不足以使冠层光谱出现明显表征的水分胁迫条件下,利用该法可能会产生较大误差,还需要结合其他途径获取的水分胁迫信息来准确确定。     

基于作物生长模型和遥感数据同化的区域玉米产量估算

为了将遥感观测到的玉米生长期间作物冠层方向反射波谱的时间序列变化信息用于区域玉米产量估算,该文将时间序列中分辨率成像光谱仪(moderate resolution imaging spectroradiometer,MODIS)数据和高空间分辨率LandsatTM遥感观测数据相结合,以叶面积指数(LAI)作为耦合作物生长模型(crop environment resource synthesis-Maize,CERES-Maize)和植被冠层反射率模型(scattering by arbitrarily inclined leaves,SAIL)的关键参数,提出了将耦合模型与时间序列遥感观测数据同化进行区域玉米产量估算的方案。该文选择吉林省榆树市为研究区,采用MODIS和LandsatTM2种尺度数据集,利用SCE-UA(shuffled complex evolution method developed at the University of Arizona)算法分别进行玉米产量同化估产研究,得到玉米单产空间分布的估计结果,结合遥感估算的种植面积求算榆树市玉米总产量。结果表明,与玉米统计总产量相比,2007、2008和2009年遥感数据同化估算的总产量误差分别为9.15%、14.99%和8.97%;与仅利用CERES-Maize模型模拟得到的产量误差相比,3a间遥感估算总产量的误差分别减小了7.49%、1.21%和5.23%,且采用MODIS和TM遥感数据估算的玉米产量表现了其空间差异性。利用榆树市3a间玉米产量的明显差异,分析了时序遥感数据对作物长势和产量变化信息的表达能力,同年份内时序归一化差值植被指数越大,对应的玉米产量越高;年际间遥感观测反射率的差异通过数据同化方法能够反映年际间玉米产量差的变化。该文提出的玉米估产方案为将来进一步结合多源遥感数据、植被冠层反射率模型与作物生长模型进行区域玉米估产研究提供了参考。     

冬小麦、夏玉米水分胁迫监测系统

根据冬小麦、夏玉米田间试验结果分析了作物水分胁迫判别指标叶水势、冠层温度-气温差变化规律,在充分考虑判别指标随作物发育期变化的特征基础上,提出了叶水势、冠层温度-气温差反映土壤相对含水量的指标,并建立了作物水分胁迫监测系统,对指导生产实践具有一定作用。     

冬小麦,夏玉米水分胁迫监测系统

根据冬小麦、夏玉米田间试验结果分析了作物水分胁迫判别指标叶水热、冠层温度－气温差变化规律,在充分考虑判别指标随作物发育期变化的特征基础上,提出了叶水势、冠层温度－气温差反映土壤相对含水量的指标, 作物水分胁迫监测系统,对指导生产实践具有一定作用。     

基于遥感蒸散发的区域作物估产方法

灌区作物产量估算对农业用水效率评价和灌区水分管理具有重要意义。该研究以干旱区代表性灌区-内蒙古河套灌区主要农作区为研究对象,基于遥感蒸散发模型HTEM和遥感作物识别结果获取河套灌区玉米生育期日蒸散发量。选取Jensen模型、Blank模型和Stewart模型3种常用水分生产函数模型,建立河套灌区玉米估产模型,并分析各估产模型的适用性及其参数。结果表明,研究区玉米生育期多年平均蒸散发量约为526 mm。3个模型均有较高的估产精度,其中Stewart模型的产量模拟精度最高,相对误差为4.30%,相关系数为0.75。因此,Stewart模型在河套灌区具有更好的适用性,基于遥感蒸散发模型、遥感作物识别模型和作物水分生产函数模型建立灌区作物估产模型可以取得良好的模拟效果。     

基于基因表达式编程的作物水分生产函数构建

作物水分生产函数的确定是农业水资源优化配置的关键。该研究采用农业水文生态系统模型（Agro-Hydrological & Chemical and Crop systems simulator, AHC）与基因表达式编程（Gene Expression Programming, GEP）相结合的方法构建作物水分生产函数。以河套灌区3种主要作物（葵花、玉米、小麦）为研究对象,采用AHC模型模拟作物产量等,构建基于GEP算法的作物水分生产函数,探讨考虑盐分胁迫的作物水分生产函数构建的思路与方法。结果表明：1）作物模拟产量与地下水埋深、地下水矿化度和灌水量等因素有关。2）构建作物水分生产函数的最优输入因子组合为地下水埋深、灌溉量、蒸散发、地下水矿化度、土壤根层盐分对作物胁迫因子、土壤根层含水率。3）应用作物水分生产函数估算不同灌溉定额条件下作物产量（预测产量）,并与AHC模型计算的产量（模拟产量）进行比较,玉米、葵花、小麦预测产量与模拟产量具有很好一致性,其决定系数分别是0.96、0.93、0.96,平均相对误差均小于5%,满足计算精度要求。因此,该研究所构建的作物水分生产函数可以较准确地估算盐分胁迫下作物产量,为农业节水与灌溉水高效利用提供科学参考。     

玉米作物系数无人机遥感协同地面水分监测估算方法研究

该文研究不同水分胁迫条件下无人机遥感与地面传感器协同估算玉米作物系数的可行性。利用自主研发的六旋翼无人机遥感平台搭载多光谱传感器获取内蒙古达拉特旗昭君镇试验站不同水分胁迫下大田玉米冠层光谱影像,计算植被指数,采用经气象因子和作物覆盖度校正后的FAO-56双作物系数法计算玉米的作物系数,研究作物系数与简单比值植被指数(simple ratio index,SR)、叶面积指数(leaf area index,LAI)和表层土壤含水率(surface soil moisture,SM)的相关关系,结果表明,作物系数与SR、LAI和SM的相关程度与水分胁迫程度有关,但均呈现出显著或极显著的线性关系,说明了基于这些指标建立作物系数估算模型的可能性。利用逐步回归分析方法建立了作物系数的估算模型,其估算模型,修正的决定系数、均方根误差和归一化的均方根误差分别为0.63、0.21、25.16%。经验证,模型决定系数、均方根误差和归一化的均方根误差分别为0.60、0.21、23.35%。研究结果可为利用无人机多光谱遥感平台进行作物系数估算提供技术参考。     

夏玉米水分胁迫判别指标的研究

通过定点实验，研究了夏玉米不同水分胁迫条件下叶水势、冠层温度的变化特征，结果表明，土壤水分胁迫使夏玉米的叶水势降低，冠层温度升高，冠气温差增大。通过与良好供水的田块对照表明，叶水势和冠层温度可以作为作物水分胁迫的判别指标。     

红外遥感估算春小麦农田土壤含水率的试验研究

根据水分亏缺条件下作物蒸发蒸腾量计算公式及作物水分胁迫指标CWSI(Crop Water Stress Index)的定义，提出了基于遥感作物冠层温度和土壤水分修正系数的春小麦田土壤含水率估算公式，其中土壤水分修正系数采用了幂函数形式。用该公式对春小麦田土壤含水率在分蘖后-拔节抽穗期、抽穗开花-灌浆期和乳熟-黄熟期3个生育阶段进行了估算，并对估算值和实测值进行对比和误差分析，结果表明该模型估算春小麦根层土壤含水率误差保持在18%以内。     

基于遥感信息与作物模型集合卡尔曼滤波同化的区域冬小麦产量预测

区域作物产量预测是国家粮食安全评估的重要内容。遥感虽能获取大面积地表信息,却难以反映作物生长发育的内在过程。作物生长模型已经在单点尺度能成功模拟作物的生长发育过程,但是区域尺度作物关键参数的获取仍很困难。遥感信息与作物模型结合的数据同化已经成为区域产量预测的最有效途径。该文选择河北省衡水地区冬小麦为研究对象,在WOFOST模型标定与区域化的基础上,利用WOFOST模型描述冬小麦生育期内叶面积指数(LAI)变化规律。针对MODIS数据的混合像元造成反演的LAI产品偏低的系统误差,利用实测LAI样本点融合MODIS-LAI趋势信息修正MODIS-LAI数据产品。采用集合卡尔曼(EnKF)算法同化冬小麦返青到抽穗期的MODIS-LAI与WOFOST模拟的LAI以获得时间序列最优的LAI,并以此重新驱动WOFOST模型估算区域冬小麦产量。结果表明,EnKF同化后的冬小麦产量比未同化的产量预测精度有显著提高,与县平均统计产量相比,在潜在模式下,决定系数由0.13提高到0.38,均方根误差由2480下降到880kg/hm2。研究表明,遥感信息与作物模型的EnKF同化是1种有效的作物产量预测方法,并在区域尺度应用上具有广阔的应用潜力。该研究可为区域尺度的作物估产提供参考。     

夏玉米冠气温差及其影响因素关系探析

测定了夏玉米主要生育期四个不同水分处理的冠层温度、气温、土壤含水率、叶面积指数和株高,分析了冠气温差与土壤含水率、叶面积指数及株高间的关系。结果表明:不同的灌溉水质和灌溉量措施对夏玉米冠气温差有显著的影响;中午12~14时左右H1高度(2/3株高)处的冠气温差较H2高度(H1+50 cm)更能反映作物和土壤的水分特征,可以用此时刻的卫星遥感冠层温度结合地面气象站数据监测作物和土壤的旱情;80~100 cm土层的土壤体积含水率与节水灌溉处理的冠气温差之间存在良好关系(α=0.05),0~80 cm四个土层中以中午20 cm和40 cm处的土壤体积含水率与冠气温差相关关系最好且稳定,可以利用此关系评价作物的缺水状况;充足灌溉下的夏玉米主要生育期的叶面积指数与冠气温差也有显著的相关关系,节水灌溉下二者关系不显著,说明水分充足条件下叶面积指数对冠气温差的影响更大;株高与不同水分处理的冠气温差也有一定的相关关系,冠层2/3高度处二者的相关系数分别为:0.7027(淡水节水处理)、0.4150(淡水充足处理)、0.3683(咸水节水处理)、0.3062(咸水充足处理)。这为区域上遥感反演夏玉米冠气温差进而监测农田蒸散和土壤含水率提供了试验依据。     

Ursachen kleinräumiger Ertragsschwankungen im bayerischen Tertiärhügelland und Folgerungen für eine teilschlagbezogene Düngung

Site effects of small-scale yield variation in the Tertiary hills north of Munich (Germany) and conclusions for site specific farming The effect of numerous soil factors on small-scale yield variation of winter wheat and spring barley were examined: soil structure and soil texture, soil nitrate content and soil water at different times, P_CAL-, K_CAL-, N_t- and C_t-content, pH, soil microbiology characteristics, relief, root growth and important plant diseases. The varying annual influence of soil parameters on crop yield was interrelated with climatic factors. In soils with low sand content soil productivity was largely influenced by soil structure. This effect was less pronounced on soils with medium sand content. On sandy soils, however, yield was reduced by available water capacity. Yield potential was also lowered by frequent cereal growing associated with take-all root desease of winter wheat. High yield variation from year to year confirmed that a site-specific crop management should consider annual variability of yield in addition to soil conditions and yield measurement. Site-specific N fertilization should be adapted to the actual progress of plant growth.     

Organic Nitrogen Source Addition for Improving the Physicochemical Properties of Sandy Loam Soil and Maize Performance

Poor quality of sandy loam soils ?is the main reason for low crop yield. Improvement of physicochemical properties of these soils is very challenging. Addition of organic sources may improve the soil properties. Therefore, this study investigated the adequacy of poultry-manure-compost (PMC) and pressmud-compost (PrMC) at 0 (control), 2, 4, 6, 8, and 10 t ha^?1 for improving the physicochemical properties of sandy loam soil and maize performance. An increasing trend in most soil and crop traits was seen with increasing compost levels. For 10 t PMC ha^?1, soil inorganic-N (512%), organic-carbon (78%), and water-holding capacity (65.36%) improved maximum. This resulted in the maximum mean crop growth rate (43.85%), stover yield (94%), grain protein (21%), and nitrogen use efficiency (30.6 kg kg^?1). Contrarily, grain oil (?7%) was lowest at 10 t PMC ha^?1. Consequently, 10 t PMC ha^?1 could be much effective to improve the physicochemical properties of sandy loam soils and maize performance.     

遥感与作物生长模型数据同化应用综述

遥感是获取大面积地表信息最有效的手段,在农业资源监测、作物产量预测中发挥着不可替代的重要作用;作物生长模型能够实现单点尺度上作物生长发育的动态模拟,可对作物长势以及产量变化提供内在机理解释。遥感信息和作物生长模型的数据同化有效结合二者优势,在大尺度农业监测与预报上具有巨大的应用潜力。该文系统综述了遥感与作物生长模型的同化研究,概述了遥感与作物生长模型数据同化系统的构建,在归纳国内外研究进展的基础上,总结了当前主流同化方法的特点以及在不同条件下的同化效果。进而具体分析影响同化精度的关键环节,明确了相关科学概念,并相应指出改善精度的策略或者方向。最后从多参数协同、多数据融合、动态预测、多模型耦合以及并行计算环境5个方面展望了遥感与作物生长模型数据同化的未来研究重点和发展趋势,同时结合农业应用现实需求,介绍一种数据同化与集合数值预报结合的应用框架,为大区域、高精度同化研究提供新的思路与借鉴。     

土壤类型和施氮量对连作春玉米产量及氮素平衡的影响

以吉林省春玉米连作体系为研究对象,采用多因素方差分析的方法,对多年田间定位试验结果进行分析比较,以探讨土壤类型变异对土壤―作物系统氮素平衡的影响。通过在相同气候条件下,2种土壤类型(黑土(黏化湿润均腐土)和风砂土(湿润冲积新成土))上开展的连续4年的氮肥施用量(0、168、312 kg hm~(-2))田间定位试验,研究了不同土壤类型间玉米产量、氮素矿化、残留及氮素表观损失的差异。结果表明,土壤类型显著影响玉米产量,黑土的玉米籽粒产量较高(6 469~10 106 kg hm~(-2)),平均为8 623 kg hm~(-2),风砂土的玉米籽粒产量较低(1 386~8 196kg hm~(-2)),平均为5911 kg hm~(-2);黑土和风砂土玉米籽粒产量的年际间(2009―2012年)变异系数分别为13.4%和59.1%,黑土的玉米籽粒产量稳定性显著大于风砂土;黑土连续4季氮素总表观矿化量为328 kg hm~(-2),为风砂土的2.2倍;受土壤质地影响,黑土收获后0~100 cm土层土壤矿质氮残留量为99~321 kg hm~(-2),显著高于风砂土(38~77 kg hm~(-2));在中等施氮(168 kg hm~(-2))条件下,黑土与风砂土的氮肥表观损失量无显著差异,分别为320 kg hm~(-2)和315 kg hm~(-2);当施氮量增加至312 kg hm~(-2)时,黑土和风砂土的氮肥表观损失量均显著增加,且风砂土的氮肥表观损失量达到827 kg hm~(-2),显著高于黑土。由于受土壤质地和土壤供肥能力的影响,土壤类型会对玉米产量、氮素矿化和表观损失有一定的影响,因此,在氮肥优化管理中应考虑土壤类型的变异。     

Estimation of Sunflower Seed Yield Using Partial Least Squares Regression and Artificial Neural Network Models

Statistical models can efficiently establish the relationships between crop growth and environmental conditions while explicitly quantifying uncertainties. This study aimed to test the efficiency of statistical models established using partial least squares regression(PLSR) and artificial neural network(ANN) in predicting seed yields of sunflower(Helianthus annuus). Two-year field trial data on sunflower growth under different salinity levels and nitrogen(N) application rates in the Yichang Experimental Station in Hetao Irrigation District, Inner Mongolia, China, were used to calibrate and validate the statistical models. The variable importance in projection score was calculated in order to select the sensitive crop indices for seed yield prediction. We found that when the most sensitive indices were used as inputs for seed yield estimation, the PLSR could attain a comparable accuracy(root mean square error(RMSE) = 0.93 t ha-1, coefficient of determination(R~2) = 0.69) to that when using all measured indices(RMSE = 0.81 t ha-1,R~2= 0.77). The ANN model outperformed the PLSR for yield prediction with different combinations of inputs of both microplots and field data. The results indicated that sunflower seed yield could be reasonably estimated by using a small number of crop characteristic indices under complex environmental conditions and management options(e.g., saline soils and N application). Since leaf area index and plant height were found to be the most sensitive crop indices for sunflower seed yield prediction, remotely sensed data and the ANN model may be joined for regional crop yield simulation.     

Evaluation of Dolomite Phosphate Rock–N‐Viro Soil Mixtures for Growth of a Horticultural Crop in an Acidic Sandy Soil

Abstract

Most agricultural soils in the Indian River area, South Florida, are sandy with minimal holding capacity for moisture and nutrients. Phosphorus (P) leaching from these soils has been suspected of contributing to the eutrophication of surface waters in this region. Dolomite phosphate rock (DPR) and N‐viro soil are promising amendments to increase crop production and reduce P loss from sandy soils. Soil incubation and greenhouse pot experiments were conducted to examine the effects of Florida DPR–N‐viro soil mixtures on the growth of a horticultural crop in an acidic sandy soil and to generate information for developing a desired formula of soil amendments. Dolomite phosphate rock and N–viro soil application increased soil pH, electrical conductivity (EC), extractable P, calcium (Ca), and magnesium (Mg). N–viro soil had greater effect on soil pH, organic matter content, and microbial biomass than the DPR. Comparatively higher nitrification rates were found in the N–viro soil treatment than the DPR treatment. A systematic decrease in soil‐extractable P was found with increasing proportions of N‐viro soil from the combined amendments. Greenhouse study demonstrated that the application of DPR and N‐viro soil significantly improved dry‐matter yield and increased plant P, Ca, and Mg concentrations of radish (Raphanus sativus L.). Based on dry‐matter yield and plant N uptake, the combined amendments that contained 30% or 20% of DPR materials appear to be optimal but remain to be confirmed by field trials.     

毛管埋深和层状质地对番茄滴灌水氮利用效率的影响

为了确定不同质地土壤中地下滴灌的适宜毛管埋深,通过两年日光温室滴灌施肥灌溉试验,研究了毛管埋深、土壤层状质地和施氮量对番茄产量、品质及水分利用效率（WUE）、氮肥表观利用率（AFUE）和氮肥偏生产力（PFP）的影响。研究结果表明,均质壤土中,毛管埋深、施氮量及其交互作用对番茄产量和WUE影响不显著,地下滴灌番茄Vc含量比地表滴灌低;番茄AFUE随毛管埋深增加而降低,施氮量由150 kg/hm2增加到225 kg/hm2时,番茄PFP显著降低。对均质壤土,建议毛管埋深15 cm、施氮量150 kg/hm2,以获得较高的PFP。土壤的层状质地结构明显降低番茄的产量、WUE和PFP,与均质壤土处理相比,上砂下壤和砂土夹层处理WUE分别低32%和11%,产量和PFP分别低33%和12%,从提高水氮利用效率的角度出发,建议在上粗下细（上砂下壤）的层状土壤中慎重使用地下滴灌。     

High Rate of Nitrogen Fertilization Increases the Crop Water Stress Index of Corn under Soil Drought

Soil nitrogen (N) availability is dominated by soil water regime and the N fertilizer levels, which affect crop growth in soil water stress. To determine the optimum N applications under different degrees of soil drought, this study investigated the effects of N fertilizer levels on the crop water stress index (CWSI) of summer corn under soil water stress. A 2-year field experiment was conducted in waterproof plots in upland red soils in subtropical China. Three N fertilizer levels and seven soil water deficit levels were employed in 2007 and 2008. Nitrogen fertilization had no influence on the CWSI of the corn under slight to moderate soil drought, but the high-N treatment increased the CWSI significantly (P < 0.01) under soil drought when the mean CWSI exceeded ～0.20. The results suggested that for scheduling irrigation or predicting crop yields, the equations between CWSI and yield should be established on comparable N fertilization levels.