摘要
为对比分析Prosail模型和Landsat 8数据在植被含水量反演中的效果,以冠层等效水厚度为植被含水量指标,首先基于地面实测植被参数和Landsat 8波谱响应函数,得到基于Prosail模型的宽波段反射率,并基于模拟宽波段数据和TM8卫星数据构建归一化植被指数(NDVI)、增强型植被指数(EVI)及两种归一化差值水分指数(NDWI),评价每种指数与小麦冠层含水量的相关性,再基于模拟植被数据、TM8植被数据和小麦冠层含水量,开展植被水分含量的建模和验证分析。结果表明,基于Prosail模型模拟得到的NDWI5和基于Landsat 8构建的NDWI5在小麦冠层含水量反演中的精度均优于NDVI、EVI和NDWI7,且二者的反演精度较为一致,可为地面实测数据过少的区域植被冠层含水量遥感反演提供一种新的思路。
Water content is one of the important indicators for vegetation health.The effect of Prosail model and Landsat 8 data on vegetation water content inversion was analyzed by using the equivalent water thickness of canopy as the index of vegetation water content.Firstly,based on the ground measured vegetation parameters and the Landsat 8 spectral response function,the wide band reflectance was obtained according to Prosail model output reflectance,and the normalized vegetation index(NDVI),the enhanced vegetation index(EVI)and two normalized difference water index(NDWI)indices were constructed based on the simulation of wideband data and TM8 satellite data,respectively.Then,we evaluate the correlation coefficient between each index and water content.The results showed that the accuracy of the model based on NDWI5 was better than NDVI,EVI and NDWI7 in the inversion of vegetation canopy water content,and performance of NDWI5 from Prosail and Landsat 8 was consistent in the inversion of vegetation water content.The result of this paper can provide a new idea for remote sensing inversion of vegetation canopy water content in areas with few measured data.
作者
侯学会
王猛
刘思含
高帅
隋学艳
梁守真
万华伟
HOU Xuehui;WANG Meng;LIU Sihan;GAO Shuai;SUI Xueyan;LIANG Shouzhen;WAN Huawei(Institute of Agriculture Sustainable Development,Shandong Academy of Agriculture Sciences,Jinan,Shandong 250100,China;Key Laboratory of East China Urban Agriculture,Ministry of Agriculture,Jinan,Shandong 250100,China;Satellite Environment Center,Ministry of Environmental Protection,Beijing 100094,China;State Key Laboratory of Remote Sensing Science,Institute of Remote Sensing and Digital Earth,Chinese Academy of Sciences,Beijing 100101,China)
出处
《麦类作物学报》
CAS
CSCD
北大核心
2018年第4期493-497,共5页
Journal of Triticeae Crops
基金
国家高分辨率对地观测重大专项项目(30-Y20A34-9010-15/17)
山东省自然科学基金项目(ZR2014YL016)
