干旱区生态环境知识资源中心(Arid): 基于Sentinel-2数据的干旱区典型绿洲植被叶绿素含量估算

Arid

	基于Sentinel-2数据的干旱区典型绿洲植被叶绿素含量估算
其他题名	Estimation of Chlorophyll Content of Typical Oasis Vegetation in Arid Area Based on Sentinel-2 Data
	顾峰 1; 丁建丽1 ; 葛翔宇 1; 高石宝 2; 王敬哲 1
来源期刊	干旱区研究
ISSN	1001-4675
出版年	2019
卷号	36 期号:4 页码:924-934
中文摘要	以渭干河库车河绿洲(渭库绿洲)为研究区,采用在机器学习方面具有明显优势的随机森林回归算法,对绿洲内的4种典型植被(棉花、芦苇、杨树、大枣)叶片的叶绿素相对含量(soil and plant analyzer development,SPAD)进行估算和验证。首先基于红边处光谱信息丰富的哨兵2号(Sentinel-2)影像和由其衍生的一阶微分、二阶微分影像各提取23种对叶绿素敏感的宽波段光谱指数,加入3种影响植物生长的土壤参量(土壤含水量,土壤有机质,土壤电导率)作为影响叶片SPAD的特征变量,再根据以上特征变量对每种植被叶片各建立3种方案的 SPAD估算模型,从而实现对绿洲内植被叶绿素的监测。结果表明:①影像经一阶微分再提取的植被指数相比原位光谱植被指数,在SPAD估测模型中起到了更重要的作用,在随机森林算法的重要性排序中位居前列;② 4种植被叶片的SPAD估测模型都取得了不错的效果,芦苇叶片尤为显著,确定系数(R~2)达到了0.926;③分析对比3种方案下模型预测能力,方案3(包含土壤参量)的预测能力卓越〔2.143 <相对百分比偏差(RPD)<2.692〕,其预测能力排序为:方案3 >方案1 >方案2,土壤属性和模型预测结果有较强的非线性相关。Sentinel-2数据具有理想的估算绿洲植被叶绿素含量的潜力,提供了一种高效、低成本、潜在高精度的方案来估算叶绿素含量,可为干旱区绿洲农业、生态系统实现更有效的保护和管理提供参考。
英文摘要	The Ogan-Kuqa River Delta Oasis,a typical oasis in the arid zone in China,was taken as the study area.The method of Random Forest with a comparative advantage in machine learning was chosen to model and estimate the relative contents of chlorophyll (SPAD values) of leaves from four kinds of representative vegetation (cotton,reed,poplar and jujube).The 23 broadband spectral indices of vegetation,which are sensitive to chlorophyll content,were obtained based on the reflectance of original Sentinel-2 image with rich spectral information in thered edgebands.These vegetation indices were extracted again in the original band order on the firstly-derived Sentinel-2 image and secondly-derived Sentinel-2 image.Three soil parameters (soil moisture content,SMC;soil organic matter,SOM;electrical conductivity,EC) related to vegetation growth were all conducted as the characteristic variables affecting SPAD values.According to the characteristic variables above,three modelling schemes could be developed to monitor the SPAD values of vegetation leaves in oasis.The results showed that:① Vegetation indices obtained from the firstly-derived image played a more important role than the original vegetation indices in the SPAD estimation model.② It could be concluded that SPAD-RF regression model,based on the Sentinel-2 satellite image data,could be used to effectively monitor the SPAD values of leaves of the four vegetation types.Especially for the estimation model of SPAD of reed leaves,R~2 reached 0.926.③ By analyzing and comparing the model prediction capability under the three schemes,the prediction capability of scheme 3 (including soil parameters) was excellent (2.143 scheme 1 >scheme 2.There was a significant nonlinear correlation between the soil properties and the model prediction results.Holistically,Sentinel-2 data has great potential for predicting chlorophyll content of oasis vegetation.This study provided an efficient,low-cost,potentially high-precision solution to estimate SPAD.
中文关键词	绿洲 ; Sentinel-2数据 ; 叶绿素 ; 植被指数 ; 随机森林 ; 新疆
英文关键词	SPAD oasis Sentinel-2 data SPAD chlorophyll vegetation index random forest Xinjiang
语种	中文
国家	中国
收录类别	CSCD
WOS类目	AUTOMATION CONTROL SYSTEMS
WOS研究方向	Automation & Control Systems
CSCD记录号	CSCD:6537132
来源机构	新疆大学
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/239176
作者单位	1.新疆大学资源与环境科学学院;;新疆大学;;新疆大学, ;;智慧城市与环境建模新疆自治区普通高校重点实验室;;绿洲生态教育部重点实验室, 乌鲁木齐;;乌鲁木齐;;乌鲁木齐, 新疆;;新疆;;新疆 830046;;830046;;830046, 中国; 2.浙江大学地球科学学院, 杭州, 浙江 310027, 中国
推荐引用方式 GB/T 7714	顾峰,丁建丽,葛翔宇,等. 基于Sentinel-2数据的干旱区典型绿洲植被叶绿素含量估算[J]. 新疆大学,2019,36(4):924-934.
APA	顾峰,丁建丽,葛翔宇,高石宝,&王敬哲.(2019).基于Sentinel-2数据的干旱区典型绿洲植被叶绿素含量估算.干旱区研究,36(4),924-934.
MLA	顾峰,et al."基于Sentinel-2数据的干旱区典型绿洲植被叶绿素含量估算".干旱区研究 36.4(2019):924-934.