农田春小麦叶面积指数和覆盖度时空变异性研究

准确获取春小麦叶面积指数和覆盖度的时空变异特征,对春小麦生长参数时空分析至关重要,也是利用遥感准确反演春小麦叶面积指数和覆盖度必须解决的问题,对于尺度转换研究具有十分重要的意义。综合运用传统统计分析方法、地质统计分析方法及时间稳定分析方法,研究了春小麦叶面积指数和覆盖度在不同生育阶段的时空变异特征,并探讨了二者的关系,建立了综合考虑时空特征的春小麦叶面积指数增长模型。研究结果表明:在研究条件下,春小麦覆盖度和叶面积指数随时间的变化趋势相似,但二者变异系数(CV)的变化趋势明显不同,随着春小麦的不断生长,覆盖度CV不断减小,而叶面积指数CV则是先增加后减小;春小麦叶面积指数和覆盖度都具有空间结构,其中在播种-分蘖阶段(头水灌溉前)的空间相关距离最大(50~60 m),头水灌溉后,春小麦叶面积指数和覆盖度的空间相关距都减小,其中叶面积指数相对比较稳定(约20 m);春小麦叶面积指数和覆盖度均具有时间稳定特征,播种-分蘖阶段处于头水灌溉前,这个阶段的春小麦覆盖度对其在整个生育期的稳定性有显著影响,相比之下,这个阶段的叶面积指数对其在整个生育期的稳定性影响不明显;春小麦叶面积指数除了与生育期有密切的时间相关关系外,还在一定范围内与覆盖度有显著的空间相关关系,为此从时空变异角度,建立了一个以生育期和覆盖度为预报因子的叶面积指数增长模型,经检验,拟合模型方程在置信度0.01水平上表现显著。叶面积指数增长模型将不同时间的叶面积结合了空间上的变异特征,较之前的仅基于生育期的Logistic模型适应性更广。

Spatial and Temporal Variability of Spring Wheat Leaf Area Index and Coverage in Northwest China

The temporal and spatial variability of spring wheat leaf area index (LAI) and coverage was investigated, which was essential for the spatial analysis of crop parameters and the exact way to resolve the matching problems between remote sensing data and ground observation data. The results showed that, with the growing of spring wheat, the CV of coverage decreased, while the CV of LAI increased first and then decreased. At the coverage-tillering stage, both wheat LAI and coverage had the maximum spatial correlation distance and the minimum spatial variation. But at later growth stages, the spatial correlation distance was relatively stable for LAI and coverage. At some sample points, spring wheat LAI and coverage had a higher temporal stability than others, especially after the tillering-shooting stage. Compared with the coverage, the temporal stability of LAI was more significant. The sampling sites with higher time stability could be used to estimate the mean value of large region. Spring wheat LAI model was built with parameters of coverage and growth period, which could reflect the spatial and temporal variability.