Determination of critical nitrogen dilution curve based on leaf area index for winter wheat in the Guanzhong Plain, Northwest China

Excessive use of nitrogen (N) fertilizers in agricultural systems increases the cost of production and risk of environmental pollution. Therefore, determination of optimum N requirements for plant growth is necessary. Previous studies mostly established critical N dilution curves based on aboveground dry matter (DM) or leaf dry matter (LDM) and stem dry matter (SDM), to diagnose the N nutrition status of the whole plant. As these methods are time consuming, we investigated the more rapidly determined leaf area index (LAI) method to establish the critical nitrogen (Nc) dilution curve, and the curve was used to diagnose plant N status for winter wheat in Guanzhong Plain in Northwest China. Field experiments were conducted using four N fertilization levels (0, 105, 210 and 315 kg ha?1) applied to six wheat cultivars in the 2013–2014 and 2014–2015 growing seasons. LAI, DM, plant N concentration (PNC) and grain yield were determined. Data points from four cultivars were used for establishing the Nc curve and data points from the remaining two cultivars were used for validating the curve. The Nc dilution curve was validated for N-limiting and non-N-limiting growth conditions and there was good agreement between estimated and observed values. The N nutrition index (NNI) ranged from 0.41 to 1.25 and the accumulated plant N deficit (Nand) ranged from 60.38 to –17.92 kg ha?1 during the growing season. The relative grain yield was significantly affected by NNI and was adequately described with a parabolic function. The Nc curve based on LAI can be adopted as an alternative and more rapid approach to diagnose plant N status to support N fertilization decisions during the vegetative growth of winter wheat in Guanzhong Plain in Northwest China.

Effects of Nitrogen Management on the Yield of Winter Wheat in Cold Area of Northeastern China

A plot experiment including four treatments, CK （N 105 kg ha-1 as urea, including a basal N application of 35 kg ha-I and a topdressing N 70 kg ha-1 at turned green stage） and optimized N management （OPT1, OPT2 and OPT3, applied two-thirds, one-third and two-fifths N at jointing stage, respectively, total N 60 kg ha-l）, was conducted to evaluate the effects of nitrogen management on growth and N uptake of winter wheat （Triticum aestivum）, Dongnong 1, which is the first highly cold tolerant winter wheat in China. Index of population quality, N uptake and yield were determined. The ear-bearing tiller rate was increased by above 12%, and the leaf area index, biomass and N uptake were significantly decreased （P〈O.05） at jointing stage. OPT treatments increased the grain to leaf area ratio at heading stage, the dry matter weight and N uptake after heading by 14.3-27.9%, 11.6-28.7% and 118.1-161.8 %, respectively. The yield of the OPT treatments was increased by 14.2-37.5% compared with CK, and there was a significant difference （P〈0.05） between CK and OPT1 treatments. Harvest index and N partial factor productivity （PFP, kg grain yield per kg N applied） was clearly enhanced from 0.4 and 35.6 kg, respectively for CK to an average of 0.48 （P〈0.05） and 77.6 kg （P〈0.05） in the OPT treatments. These results indicated that the optimized N management increased the harvest index, yield and N use efficiency by decreasing the N application rate and postponing N application time, improved wheat population quality, controlled excessive growth in the vegetative stages and increased dry matter and N accumulation rates after heading.

The Stimulating Effects of Rewatering on Leaf Area of Winter Wheat Suffering Water Stress

After water stress at various levels and durations at different growth stages, rewatering could greatly stimulate the leaf area development of winter wheat. The results showed that the stimulation effect changed with water stress time, degree and duration. Rewatering under earlier stress had greater stimulation effect on leaf area than that under later stress. Higher stimulation effect was observed under severe water stress than that under moderate stress. Longer duration of stress resulted in low stimulation effect. In spite of the greater stimulation effect under severe and longer stress, the final leaf area in these situations was lower than that under moderate stress and shorter duration. Whenever the stress occurred, the stimulating effect was due to the increase of the leaf area of the tillers. Once the leaf on the main stem emerged during stress period, rewatering had no effect on its size, and consequently its leaf area. The stimulation of rewateirng on leaf area contributed to the final grain yield by 45% under moderate stress, and 67% under severe stress. Although the stimulation partly compensated for the loss during stress, the final leaf area and the grain yield could not reach the level without water stress.

Mapping Spatial and Temporal Variations of Leaf Area Index for Winter Wheat in North China

Leaf area index （LAI） is an important parameter in a number of models related to ecosystem functioning, carbon budgets, climate, hydrology, and crop growth simulation. Mapping and monitoring the spatial and temporal variations of LAI are necessary for understanding crop growth and development at regional level. In this study, the relationships between LAI of winter wheat and Landsat TM spectral vegetation indices （SVIs） were analyzed by using the curve estimation procedure in North China Plain. The series of LAI maps retrieved by the best regression model were used to assess the spatial and temporal variations of winter wheat LAI. The results indicated that the general relationships between LAI and SVIs were curvilinear, and that the exponential model gave a better fit than the linear model or other nonlinear models for most SVIs. The best regression model was constructed using an exponential model between surface-reflectance-derived difference vegetation index （DVI） and LAI, with the adjusted R2 （0.82） and the RMSE （0.77）. The TM LAI maps retrieved from DVILAI model showed the significant spatial and temporal variations. The mean TM LAI value （30 m） for winter wheat of the study area increased from 1.29 （March 7, 2004） to 3.43 （April 8, 2004）, with standard deviations of 0.22 and 1.17, respectively. In conclusion, spectral vegetation indices from multi-temporal Landsat TM images can be used to produce fine-resolution LAI maps for winter wheat in North China Plain.

Effects of Ridge-Mulching with Plastic Sheets for Rainwater-Harvesting Cultivation on WUE and Yield of Winter Wheat

A field experiment was conducted in a manural loesial soil in middle of Shaanxi Province ofChina, a sub-humid area prone to drought, to study the effects of rainwater-harvestingcultivation on water use efficiency (WUE) and yield of winter wheat. Ridge-furrow tillage wasused, the ridge being mulched by plastic sheets for rainwater harvesting while seeding in thefurrows. Results showed that from sowing to reviving stage of winter wheat, water stored in 0-100 cm layer was significantly decreased whereas that in 100-200 cm layer did not change.Compared to the non-mulching, plastic mulch retained 6.5 mm more water as an average of the twoN rate treatments, having a certain effect on conservation of soil moisture. In contrast, atharvest, water was remarkably reduced in both the 0-100 cm and the 100-200 cm layers, andmulched plots consumed 34.8 mm more water as an average of the two treatments: low N rate (75kg N ha-1) with low plant density (2 300 000 plants ha-1) and high N rate (225 kg N ha-1)with highplant density (2 800 000 plants ha-1), in 0-200 cm layer than those without mulching, the formerbeing beneficial to plants in utilization of deep layer water. Mulching was significant inharvesting water and in increase of yield. Mulched with plastic sheets, biological and grainyields were 22.5 and 22.6% higher for the average of the high N rate than for the low N rate,and the high N rate with low plant density was 29.8 and 29.1% higher in both biological andgrain yields than that of the low N rate with low plant density. With high N rate and high plantdensity, the mulched biological and grain yields were 39.5 and 28.9% higher than the correspondingtreatments without mulching. Of the treatments, that with high N rate and low plant density wasthe highest in both biological and grain yields, and the water use efficiency reached 43.7 kgmm-1 ha-1 for biological yield and 22 kg mm-1 ha-1 for grain yield, being the highest WUE reportedin the world up to now.

基于CERES-Wheat和遥感数据的土壤水分供给量反演

以关中平原为研究区域,以冬小麦为研究对象,基于2007—2008年TM遥感数据反演的和CERES-Wheat模型模拟的生物量和叶面积指数,将由离散积分思想计算的日均分摊系数和由冬小麦各生育阶段生长特点划分的分段蒸腾系数引入土壤水分平衡方程,建立土壤水分供给量反演模型。利用该模型进行研究区域2007—2008年冬小麦全生育期的蒸散量和土壤水分供给量的单点和区域尺度的定量反演。选取拥有多时相遥感数据的2000—2001年进行模型验证,结果表明,在充分获取降水、灌溉信息和多时相遥感数据的条件下,土壤水分供给量的反演结果准确度较高。区域尺度的土壤水分供给量呈现出西高东低和北高南低的分布特征,自西北部向东南部逐渐递减,与关中平原冬小麦受水分胁迫程度的区域性变化趋势基本一致,表明应用模型反演冬小麦全生育期的土壤水分供给量是可行的。

基于无人机影像的冬小麦株高提取与LAI估测模型构建

株高和叶面积指数(Leaf Area Index,LAI)反映着作物的生长发育状况。为了探究基于无人机可见光遥感提取冬小麦株高的可靠性,以及利用株高和可见光植被指数估算LAI的精度,本文获取了拔节期、抽穗期、灌浆期的无人机影像,提取了冬小麦株高与可见光植被指数,使用逐步回归、偏最小二乘、随机森林、人工神经网络四种方法建立LAI估测模型,并对株高提取及LAI估测情况进行精度评价。结果显示:(1)株高提取值Hc与实测值Hd高度拟合(R^(2)=0.894,RMSE=6.695,NRMSE=9.63%),株高提取效果好;(2)与仅用可见光植被指数相比,基于株高与可见光植被指数构建的LAI估测模型精度更高,且随机森林为最优建模方法,当其决策树个数为50时模型估测效果最好(R^(2)=0.809,RMSE=0.497,NRMSE=13.85%,RPD=2.336)。利用无人机可见光遥感方法,高效、准确、无损地实现冬小麦株高及LAI提取估测可行性较高,该研究结果可为农情遥感监测提供参考。

基于HJ-1星和GF-1号影像融合特征提取冬小麦种植面积

为提高基于国产环境与灾害监测预报卫星(HJ-1/CCD)影像大范围提取冬小麦种植面积的精度,以江苏省宿迁市沭阳县为研究区域,对冬小麦拔节期30 m×30 m的HJ-1/CCD多光谱影像和2 m×2 m的高分1号卫星全色影像(GF-1/PMS)进行融合与面向对象分类研究。将GF-1/PMS全色影像进行8、16和24 m重采样,得到4种空间分辨率(含2 m)的全色影像,分别与HJ-1/CCD多光谱影像利用光谱锐化法(Gram-Schmidt,GS)进行融合。通过对融合影像进行质量评价,选择适合研究区冬小麦种植田块格局的适宜尺度影像。将HJ-1/CCD多光谱影像重采样,得到与适宜尺度融合影像相同尺度的影像,在两景影像中分别选取包含光谱、纹理信息的训练融合影像样本(samples of fused image,SFI)和重采样影像样本(samples of resampling image,SRI),采用面向对象分类方法对适宜尺度融合影像(fused image,FI)和重采样影像(resampling image,RI)进行冬小麦种植面积提取。结果表明,16 m×16 m融合影像的效果优于2 m×2 m、8 m×8 m和24 m×24 m融合影像,其均值、标准差、平均梯度和相关系数分别为161.15、83.01、4.55和0.97。面向对象分类后,SFI对重采样影像RI16m分类的总体精度为92.22%,Kappa系数为0.90。SFI对融合影像FI16m分类的总体精度为94.44%,Kappa系数为0.93。SRI对重采样影像RI16m分类的总体精度为84.44%,Kappa系数为0.80。SFI对融合影像FI16m分类效果最好,说明基于融合影像和融合影像提取样本(SFI)结合的面向对象分类方法能准确提取冬小麦种植面积。另外,重采样影像和融合影像提取样本(SFI)相结合的面向对象分类方法也可较好提取冬小麦种植面积。为利用国产中空间分辨率HJ-1/CCD卫星和高分1号卫星融合影像有效提取大区域冬小麦种植面积信息提供了参考。

基于Sentinel-1/2数据特征优选的冬小麦种植区识别方法研究

为了提高冬小麦种植区识别精度,本文基于谷歌地球引擎(Google Earth Engine,GEE)平台和随机森林算法,对比雷达和光学遥感数据对冬小麦提取效果的差异,并对多类特征变量进行重要性分析,研究特征优选对冬小麦识别精度的影响。选取2019年3—5月冬小麦关键生育期的Sentinel-1和Sentinel-2影像为数据源,构建Sentinel-1的极化特征和纹理特征以及Sentinel-2的光谱特征、植被指数特征、植被指数变化率特征共5类特征变量;设置不同数据源和不同特征组合的冬小麦种植区提取方案;对方案中特征变量进行优选,得出最优特征组合,利用最优特征组合对河南省驻马店市冬小麦种植区进行提取。结果表明,无论是否进行特征优选,基于多源遥感数据的冬小麦识别精度均优于仅采用光学或雷达数据的精度;经过特征优选后,各方案的分类精度均有不同程度的提升,说明多源数据特征变量组合和特征优选均能够提高分类精度。不同月份和类型的特征变量对分类精度的贡献率不同,贡献率由大到小为4月、3月和5月;贡献率由大到小的特征类型为极化特征、植被指数变化率特征、植被指数特征、光谱特征和纹理特征。基于多源数据特征优选提取的2019年驻马店冬小麦空间分布最优,总体精度为95.60%,Kappa系数为0.93,冬小麦提取面积与统计年鉴数据相比,相对误差为2.23%。本文可为基于多源光学和雷达遥感影像进行农作物种植区提取的研究提供理论参考。

干旱胁迫下冬小麦叶面积指数的高光谱监测

为实现干旱条件下冬小麦叶面积指数(LAI)的快速精准监测,为我国干旱半干旱地区的冬小麦安全生产提供理论和技术支持,为干旱条件下冬小麦LAI的快速精准监测提供参考,以不同干旱条件下的冬小麦为研究对象,测定关键生育时期冬小麦的LAI和冠层高光谱反射率,探索冬小麦LAI和高光谱反射率间的响应趋势,分别采用一阶微分(1ST)、标准正态变换(SNV)和对数变换(Log)3种方法对原始光谱反射率(R)进行预处理;对比分析不同预处理后光谱反射率与LAI的相关关系,采用偏最小二乘回归(PLSR)算法构建不同预处理下冬小麦LAI的监测模型。结果表明,在可见光区域和1100~1800 nm光谱区域,随着LAI的升高,光谱反射率呈现出逐渐增大的趋势;3种预处理方法均改善了光谱反射率与冬小麦LAI的相关性,其中1ST预处理后的光谱反射率与LAI的相关性最高,在1209 nm处相关系数达到了0.550。基于1ST预处理后光谱反射率,采用PLSR构建的冬小麦LAI监测模型表现最佳,相较R-PLSR模型,1ST-PLSR校正模型和验证模型的决定系数分别提高了42.974%和8.842%,均方根误差分别降低了33.710%和8.111%,相对分析误差分别提高了50.838%和8.813%。

融合GF-6 WFV影像主成分分析特征的县域冬小麦种植面积提取

为准确、快速获得县域冬小麦的种植信息,针对多时相方法存在的成本高、效率低、过程复杂等问题,以安徽省固镇县为研究区,提出基于单时相GF-6 WFV影像主成分分析特征与原始光谱波段归一化融合、并使用K-最近邻算法进行土地覆盖物分类的有效面积提取方法。结果表明,所提出方法优于RAW和PDR这2种基准方法,且降维维度参数为3时效果最好,总体精度和Kappa系数分别为89.71%和0.87,实际冬小麦提取面积精度达98.49%,相对误差仅为1.51%。

基于机器学习算法的扬州市冬小麦遥感分类提取

卫星遥感技术是目前较为常用的农作物监测与分类技术。为实现区域冬小麦精确分类和面积提取,以江苏省扬州市为例,以哨兵2号卫星数据及航天飞机雷达地形测量(SRTM)高程数据为数据源,利用分类与回归决策树(CART)、梯度提升决策树(GBDT)、支持向量机(SVM)和随机森林(RF)4种机器学习算法建立分类模型。同时下载并调用2021年3月22日研究区的MSI多光谱影像,提取光谱、纹理、地形特征等参数,对研究区冬小麦进行分类提取,并分析4种模型的分类效果和精度。结果表明,RF和GBDT分类方法效果最好,总体精度最高,均为0.967,Kappa系数达0.960;SVM分类方法总体精度最低,为0.514,但用户精度最高,为0.972。上述方法可以实现区域农作物的精确分类和提取。

基于特征波段选择的冬小麦叶面积指数高光谱遥感估测模型研究

为提高冬小麦叶面积指数(LAI)的遥感估测精度,以实现其无损快速测定目标,在田块尺度设置多年定点不同冬小麦品种氮梯度试验,测定其不同生育时期冠层高光谱数据和LAI,通过原始冠层光谱数据与一阶导数预处理(first-derivative, FD)组合竞争自适应重加权采样(competitive adaptive reweighted sampling, CARS)、无信息变量消除(uninformative variable elimination, UVE)和随机蛙跳(random frog, RF)三种特征波段选择方法进行偏最小二乘回归(partial least squares regression, PLSR)高光谱估测模型构建。结果表明,一阶导数预处理在简化波段数量和提升模型精度上具有较好作用。经过与全波段数据及六种组合内部建模预测精度对比,RF在简化波段方面效果最好,FD-RF组合筛选波段数量为6个,建模的R^(2)和RMSE分别达到0.850和0.730,预测的R^(2)和RMSE分别为0.704和1.005;FD-CARS组合达到了最佳建模精度,R^(2)和RMSE分别为0.876和0.641;FD-UVE组合达到了最佳预测精度,R^(2)和RMSE分别为0.755和0.672。这说明基于特征波段选择可以进行冬小麦叶面积指数高光谱遥感模型建立与有效估测。

Impact of the shrinking winter wheat sown area on agricultural water consumption in the Hebei Plain

This study firstly analyzed the shrinkage of winter wheat and the changes of crop- ping systems in the Hebei Plain from 1998 to 2010 based on the agricultural statistic data of 11 cities and meteorological data, including daily temperature, precipitation, water vapor, wind speed and minimum relative humidity data from 22 meteorological stations, and then calcu- lated the water deficit and irrigation water resources required by different cropping systems, as well as the irrigation water resources conserved as a result of cropping system changes, using crop coefficient method and every ten-day effective precipitation estimation method. The results are as follows. 1） The sown areas of winter wheat in the 11 cities in the Hebei Plain all shrunk during the study period. The shrinkage rate was 16.07% and the total shrinkage area amounted to 49.62×10^4 ha. The shrinkage was most serious in the Bei- jing-Tianjin-Tangshan metropolitan agglomerate, with a shrinkage rate of 47.23%. 2） The precipitation fill rate of winter wheat was only 20%-30%, while those of spring maize and summer maize both exceeded 50%. The irrigation water resources demanded by the winter wheat-summer maize double cropping system ranged from 400 mm to 530 mm, while those demanded by the spring maize single cropping system ranged only from 160 mm to 210 ram. 3） The water resources conserved as a result of the winter wheat sown area shrinkage during the study period were about 15.96×10^8 m^3/a, accounting for 27.85% of those provided for Beijing, Tianjin and Hebei by the first phase of the Mid-Route of the South-to-North Water Diversion Project.

黄土高原半干旱雨养农业区冬小麦气候适宜度评价——以通渭县为例

气候适宜度可以表征气候条件对作物生长发育的影响程度。利用黄土高原半干旱区通渭县近40 a气象资料和农业气象观测资料,基于模糊数学方法,根据冬小麦各发育时段对温度、水分、光照的需求,对黄土高原半干旱雨养农业区冬小麦全生育期气候适宜度进行评价。结果表明:通渭县近40 a冬小麦全生育期温度适宜度为0.58,停止生长—返青期的温度适宜度最高,达0.95,三叶期—停止生长的温度适宜度低,仅为-0.42,年际间差异小,全生育期温度适宜度以0.019/10 a的速率上升;水分适宜度为0.46,乳熟期—成熟期的水分适宜度最高,为0.66,其次为返青期—拔节期,为0.63,年际间差异大,全生育期水分适宜度以0.017/10 a的速率上升;光照适宜度为0.62,三叶期—停止生长和返青期—拔节期的光照适宜度最高,为0.68,乳熟期—成熟期最低,为0.51,全生育期光照适宜度以0.011/10 a的速率上升;综合适宜度为0.54,返青期—拔节期的综合适宜度最高,为0.70,三叶期—停止生长最低,为-0.36,全生育期综合适宜度以0.014/10 a的速率上升。冬小麦产量与抽穗期—乳熟期温度适宜度、乳熟期—成熟期水分适宜度、乳熟期—成熟期综合适宜度呈显著相关。气候适宜度能够较准确地反映出黄土高原半干旱雨养农业区冬小麦生育期内气象条件的优劣变化。

基于GF-1卫星影像的青岛市冬小麦种植面积变化动态监测

为准确获取青岛市冬小麦种植面积及空间分布变化特点,帮助相关部门优化种植结构,合理安排农业生产,研究利用GF-1 WFV卫星遥感影像,选择青岛市2014—2021年冬小麦种植面积遥感提取的最佳时相,采用图像监督分类法提取冬小麦的种植面积,并通过分类后比较法分析小麦种植面积的动态变化。结果表明:2014—2021年青岛市冬小麦的种植面积为21.7万~24.8万hm^(2),2016年小麦种植面积最大,2020年最小,每年冬小麦种植面积波动范围为0.3万~2.7万hm^(2);遥感提取的种植面积与统计部门提供的种植面积误差较小,误差百分率不超过6.5%;小麦与其他地物类型之间相互转换的差异很小,2014—2021年逐年小麦转为其他地物的面积变化范围为8.0万~10.9万hm^(2),其他地物转为小麦的面积变化范围为7.9万~11.4万hm^(2)。

Change of winter wheat planting area and its impacts on groundwater depletion in the North China Plain

The North China Plain is one of the most water-stressed areas in China. Irrigation of winter wheat mainly utilizes groundwater resources, which has resulted in severe environmental problems. Accurate estimation of crop water consumption and net irrigation water consumption is crucial to guarantee the management of agricultural water resources. An actual crop evapotranspiration(ET) estimation model was proposed, by combining FAO Penman-Monteith method with remote sensing data. The planting area of winter wheat has a significant impact on water consumption; therefore, the planting area was also retrieved. The estimated ET showed good agreement with field-observed ET at four stations. The average relative bias and root mean square error(RMSE) for ET estimation were –2.2% and 25.5 mm, respectively. The results showed the planting area and water consumption of winter wheat had a decreasing trend in the Northern Hebei Plain(N-HBP) and Southern Hebei Plain(S-HBP). Moreover, in these two regions, there was a significant negative correlation between accumulated net irrigation water consumption and groundwater table. The total net irrigation water consumption in the N-HBP and S-HBP accounted for 12.9×10~9 m^3 and 31.9×10~9 m^3 during 2001–2016, respectively. Before and after 2001, the decline rate of groundwater table had a decreasing trend, as did the planting area of winter wheat in the N-HBP and S-HBP. The decrease of winter wheat planting area alleviated the decline of groundwater table in these two regions while the total net irrigation water consumption was both up to 28.5×10~9 m^3 during 2001–2016 in the Northwestern Shandong Plain(NW-SDP) and Northern Henan Plain(N-HNP). In these two regions, there was no significant correlation between accumulated net irrigation water consumption and groundwater table. The Yellow River was able to supply irrigation and the groundwater table had no significant declining trend.

基于语义分割网络的冬小麦遥感分类及变化分析

针对传统机器学习作物遥感分类模型泛化能力较弱等问题,本文评价并验证了不同语义分割网络在光谱特征和光谱+植被指数特征下的济源市冬小麦遥感分类模型的性能和分类精度。结果表明,较单一光谱特征,基于U-Net++和DeepLab V3+的光谱+植被指数特征模型损失函数和IoU值分别降低和提高了13.30%和7.83%、7.80%和5.54%。此外,基于U-Net++的2020—2023年冬小麦分类总体精度达93.47%~95.60%,较DeepLab V3+和随机森林分类的总体精度分别提高了0.12%~2.29%和4.84%~7.40%;景观度值也表明基于U-Net++的冬小麦分类结果具有更优的图斑完整度和紧凑度。最后,本文定量评价了不同年份冬小麦种植面积空间变化结果,为复杂地形条件下作物面积监测应用提供了方法支持。

集成学习结合多源数据预测河南省冬小麦单产

为探讨基于多源数据和集成学习算法预测冬小麦单产的可行性并确定冬小麦单产预测的最佳时间窗,该研究在河南省冬小麦生长季内划分28个不同的时间窗,使用8种不同的机器学习算法及基于Stacking的集成学习算法,利用2003—2018年的多种遥感指数数据、气象数据进行训练并预测2019—2021年单产。结果表明:引入日光诱导叶绿素荧光(solar-induced chlorophyll fluorescence,SIF)特征可以提升河南省冬小麦单产的预测效果;12月至次年5月为机器学习算法预测冬小麦单产的最佳时间窗;Stacking集成学习算法比其他单机器学习算法更适用于河南省县级尺度冬小麦单产预测,预测结果的决定系数为0.816,均方根误差和平均绝对误差分别为580.36和476.01 kg/hm^(2);河南省冬小麦实际单产的空间分布呈西低东高的趋势,预测的单产分布特征与实际单产分布特征相当。研究结果可为冬小麦单产预测提供一种新的方法,也为农作物单产预测模型构建提供新的思路。

基于TM和MODIS数据的水旱地冬小麦面积提取和长势监测

采用Mahalanobis Distance分类法提取了冬小麦种植面积,通过搭建决策树结构进行了不同灌溉类型冬小麦种植面积的提取,通过两个年份不同生育时期MODIS-NDVI的比较,分析了NDVI时间曲线与冬小麦长势的响应规律和水旱地冬小麦年同期长势。结果表明:2007年临汾地区冬小麦总种植面积为234778.5hm2,提取精度为96.96%,其中水地种植面积为107488.3hm2,提取精度为86.15%,旱地冬小麦为127290.2hm2,提取精度为86.16%。生育期内,水地冬小麦整体长势好于旱地冬小麦,通过NDVI变化斜率比较,表明随着生育期的推进,水地冬小麦NDVI达到峰值前的上升速度远大于旱地冬小麦,峰值后水地冬小麦NDVI下降速度小于旱地冬小麦。