摘要
研究利用遥感数据进行了运城地区冬小麦不同生育时期归一化差值植被指数和产量关系的研究,利用气象数据和光谱数据构建了冬小麦光谱产量模型、气象产量模型以及光谱气象产量模型。结果表明:运城地区水旱地冬小麦均以5月8日左右的NDVI值与产量相关性最好,且达极显著水平,因此该时期为建立冬小麦遥感估产模型的最佳时相。通过对冬小麦光谱产量模型、气象产量模型以及光谱气象产量模型预测效果进行的F检验,表明各模型均达到极显著水平。与其他两种模型相比,光谱气象产量模型的决定系数(R2)有明显的提高,并且相对均方根误差(RRMSE)和相对误差(RE)降低,且降低幅度较大。说明光谱气象产量模型比气象产量模型和光谱产量模型有较好的预测效果。平均单产的遥感估产值略高于统计数据值,旱地估产精度为80.91%,水地估产精度为87.72%;总产量的估算值略高于统计值,旱地精度为99.20%,水地精度为80.54%。因此,利用遥感和气象数据建立模型进行单产和总产估测是可行的,且精度更高。
The relationship between NDVI and grain yield was studied using remote sensing data at the different stages of winter wheat in Yuncheng region,and spectral yield model,meteorological yield model and spectrometeorological yield model were built. The results showed that the correlation between NDVI value of winter wheat in irrigation and dry-land on approximately May 8th and yield in Yuncheng region was the highest and extremely significant,so this period was the optimum period to establish remote sensing model of estimating yields in Yuncheng region. The spectral,meteorology and spectrometeorological yield models passed F test,and there were extremely significant level. Compared with other models,RRMSE and RE of spectrometeorological yield model apparently declined and the declining range was large,revealing better anticipating effect of spectrometeorological yield model compared to the model of spectrum. Remote sensing estimating value of average yield per unit was slightly larger than statistical value,while yield-estimating precision in dry-land was 80.91% and yield-estimating precision in irrigation-land was 87.72%. Estimating values of total yield were slightly higher than statistical values,where yield-estimating precision in dry-land was 99.20% and 80.54% in irrigation-land.
出处
《农业工程学报》
EI
CAS
CSCD
北大核心
2010年第11期183-188,共6页
Transactions of the Chinese Society of Agricultural Engineering
基金
山西省科技攻关项目(2006031114)
山西省气象局开放式研究基金项目(SX053001)
山西农业大学科研启动基金资助(XB2009016)
关键词
遥感
冬小麦
光谱
气象数据
产量
模型
remote sensing
winter wheat
spectrum
meteorological data
yield
model
