农业工程学报2017，Vol.33Issue(4)：225-233,9.DOI:10.11975/j.issn.1002-6819.2017.04.031
基于CASA模型的区域冬小麦生物量遥感估算
Remote sensing estimation of biomass in winter wheat based on CASA model at region scale
摘要
Abstract
Remote sensing can dynamically monitor crop, in real-time, all-weather, also simulate process of crop growth by extracting remote sensing parameters. It was the first step to estimate NPP (net primary productivity) for biomass estimation, and the CASA(Carnegie-Ames-Stanford Approach) model, one of the most popular biomass estimation model, was used for NPP estimation of winter wheat to realize the winter wheat biomass estimation in study area. We analyzed deeply and developed both the NDVI extracting method and FPAR algorithm based on the original CASA model. After comprehensively absorbing the experience of related literature, and the maximum value of light energy utilization efficiency was determined. Then we got an improved CASA model which was suitable for study area. The quantile fractile with winter wheat NDVI maximum probability distribution was extracted to determine NDVImax and NDVImin, and previous algorithm of improved FPAR with a correction factor was used in this paper. Solar radiation (SOL) around the area of the site data were used for the interpolation by natural neighbor spatial interpolation method. Temperature, precipitation and other meteorological data in the study area were used to calculate the real light energy utilization efficiency. Finally, we entered the above parameters into the improved CASA model to calculate winter wheat NPP.The study area is located in Handan city, Hebei province. The winter wheat at the county scale was taken as the research object. HJ-1A/B products were used as data support to estimate the winter wheat NPP and biomass of study area in 2014. The accuracy was verified. Results showed that the average NPP in March, April, May were 78, 297 and 320 g/m2, respectively. The difference was caused by growth characteristics of winter wheat in different periods. In March, winter wheat was in the green period, the leaf area of winter wheat increased gradually. In April, winter wheat was in exuberant growth period, leaf area was continued to increase, and the NPP also increased. In May, the winter wheat was gradually into flowering, grain filling, and milk stage etc, during the time most parts of NPP was more than 250 g/m2, which was consistent with wheat physiological characteristic, it showed that winter wheat grew well. And the average biomass of winter wheat in the study area was 1485 g/m2, more than half of study area was between 1500 and 2000 g/m2. The correlation between measured biomass and predicted biomass of winter wheat reached significant level,R2 was 0.8115, and the average relative error was 2.13%, the maximum error was 11.54%, the minimum error was 0.33%. Average predicted biomass was 1807.54 g/m2, the absolute error was 86.80 g/m2, compared with the average measured biomass 1720.74 g/m2. This study can provide theoretical support for estimating both winter wheat biomass and yield at country scale.关键词
生物量/遥感/作物/CASA/HJ-1A/B/最大光能利用率/冬小麦Key words
biomass/remote sensing/crops/CASA/HJ-1A/B/maximum light energy utilization efficiency/winter wheat分类
农业科技引用本文复制引用
刘真真,张喜旺,陈云生,张传才,秦奋,曾红伟..基于CASA模型的区域冬小麦生物量遥感估算[J].农业工程学报,2017,33(4):225-233,9.基金项目
国家高技术研究发展计划(863 计划)(2012AA12A307) （863 计划）
粮食公益性行业科研专项(201313009-2,201413003-7) （201313009-2,201413003-7）
河南省科技厅科技攻关项目(152102110047) （152102110047）
国家自然科学基金青年项目(41401457) （41401457）
