基于CASA模型的区域冬小麦生物量遥感估算

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201...

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Bibliographic Details
Published in农业工程学报 Vol. 33; no. 4; pp. 225 - 233
Main Author 刘真真 张喜旺 陈云生 张传才 秦奋 曾红伟
Format Journal Article
LanguageChinese
Published 河南大学环境与规划学院,开封 475004%中国科学院遥感与数字地球研究所,北京,100094 2017
黄河中下游数字地理技术教育部重点实验室,开封 475004
Subjects
CASA
HJ-1A/B
作物
冬小麦
最大光能利用率
生物量
遥感
最大光能利用率
biomass
maximum light energy utilization efficiency
生物量
CASA
HJ-1A/B
作物
remote sensing
遥感
crops
winter wheat
冬小麦
Online AccessGet full text
ISSN1002-6819
DOI10.11975/j.issn.1002-6819.2017.04.031

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Summary: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。
Bibliography:Liu Zhenzhen1,2, Zhang Xiwang1,2, Chen Yunsheng1,2, Zhang Chuancai1,2, Qin Fen1,2, Zeng Hongwei3 （1. Key Laboratory of Geospatial Technology for Middle and Lower Yellow River Regions, Ministry of Education, Kaifeng 475004, China; 2. College of Environment ＆ Planning of Henan University, Kaifeng 475004, China; 3. Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China）
11-2047/S
biomass; remote sensing; crops; CASA; H J-1 A/B; maximum light energy utilization efficiency; winter wheat
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 ex
ISSN:1002-6819
DOI:10.11975/j.issn.1002-6819.2017.04.031