基于CASA模型的区域冬小麦生物量遥感估算

发布时间：2018-04-19 22:30

本文选题：生物量 + 遥感　；参考：《农业工程学报》2017年04期

【摘要】：该文对原始CASA(carnegie-ames-stanford-approach)模型中归一化植被指数(normalized difference vegetation index,NDVI)最值提取方法及光合有效辐射吸收比(fraction of absorbed photosynthetically active radiation,FPAR)的算法进行了深入分析,并通过综合分析大量国内外文献,更加科学合理的确定了最大光能利用率的取值,最终确立了适合该研究区的CASA模型。该文以河北省邯郸市3个县域冬小麦为研究对象,以HJ-1A/B星遥感数据产品为数据支撑,采用CASA模型对研究区2014年冬小麦生物量进行了估算和精度验证,结果表明:研究区冬小麦生物量平均值为1 485 g/m~2,50%以上区域在1 500~2 000 g/m~2之间。冬小麦实测生物量与预测生物量相关性达到显著水平,R~2为0.811 5。经过50组数据分析对比,平均相对误差为2.13%,其中,最大值为11.54%,最小值为0.33%;平均预测生物量为1 807.54 g/m~2,与平均实测生物量1 720.74 g/m~2相比,绝对误差为86.80 g/m~2,为估算区域冬小麦产量提供理论支撑。
[Abstract]:In this paper, the method of extracting normalized difference vegetation NDVIs from the original CASA Carnegie-ames-stanford-approach-model and the algorithm of photosynthetic effective radiation absorption ratio of absorbed photosynthetically active radiation FPAR) are analyzed, and a large number of literatures at home and abroad are synthetically analyzed. The maximum light energy utilization ratio is determined more scientifically and reasonably, and the CASA model suitable for the study area is finally established. Taking winter wheat in three counties of Handan City, Hebei Province as the research object and HJ-1A/B star remote sensing data product as data support, the biomass of winter wheat in 2014 in the study area was estimated and verified by CASA model. The results showed that the average biomass of winter wheat in the study area was 1 485 g / m ~ 2 50% in the range of 1 500 ~ 2 000 g/m~2. The correlation between the measured biomass and the predicted biomass of winter wheat reached a significant level of 0.811. 5%. The average relative error was 2.13, in which the maximum value was 11.54, the minimum value was 0.33, the average predicted biomass was 1 807.54 g / m ~ 2, compared with the average measured biomass of 1 720.74 g/m~2. The absolute error is 86.80 g / m ~ (-2), which provides theoretical support for estimating regional winter wheat yield.
【作者单位】：黄河中下游数字地理技术教育部重点实验室;河南大学环境与规划学院;中国科学院遥感与数字地球研究所;
【基金】：国家高技术研究发展计划(863计划)(2012AA12A307) 粮食公益性行业科研专项(201313009-2,201413003-7) 河南省科技厅科技攻关项目(152102110047) 国家自然科学基金青年项目(41401457)
【分类号】：S512.11;S127

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