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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| Published in | 农业工程学报 Vol. 33; no. 4; pp. 225 - 233 |
|---|---|
| Main Author | |
| Format | Journal Article |
| Language | Chinese |
| 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 | |
| Online Access | Get full text |
| ISSN | 1002-6819 |
| DOI | 10.11975/j.issn.1002-6819.2017.04.031 |
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| Abstract | 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。 |
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| AbstractList | 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。 S1; 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1485 g/m2,50%以上区域在1500~2000 g/m2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,R2为0.8115.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1807.54 g/m2,与平均实测生物量1720.74 g/m2相比,绝对误差为86.80 g/m2,为估算区域冬小麦产量提供理论支撑. |
| Abstract_FL | 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. |
| Author | 刘真真 张喜旺 陈云生 张传才 秦奋 曾红伟 |
| AuthorAffiliation | 黄河中下游数字地理技术教育部重点实验室,开封475004 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 |
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| Author_FL | Chen Yunsheng Zhang Chuancai Zhang Xiwang Zeng Hongwei Liu Zhenzhen Qin Fen |
| Author_FL_xml | – sequence: 1 fullname: Liu Zhenzhen – sequence: 2 fullname: Zhang Xiwang – sequence: 3 fullname: Chen Yunsheng – sequence: 4 fullname: Zhang Chuancai – sequence: 5 fullname: Qin Fen – sequence: 6 fullname: Zeng Hongwei |
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| Keywords | 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 |
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| Notes | 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 |
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| Publisher | 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 黄河中下游数字地理技术教育部重点实验室,开封 475004 |
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| SubjectTerms | CASA HJ-1A/B 作物 冬小麦 最大光能利用率 生物量 遥感 |
| Title | 基于CASA模型的区域冬小麦生物量遥感估算 |
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