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基于地表温度和植被指数的农业干旱遥感监测方法 被引量:50
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作者 柳钦火 辛景峰 +2 位作者 辛晓洲 田国良 杨贵军 《科技导报》 CAS CSCD 2007年第6期12-18,共7页
农业旱灾是人类面临的最主要自然灾害之一,对我国农业生产影响非常大。土壤含水量是农业干旱监测的重要指标,通过遥感地表温度(LST)与植被指数(NDVI)结合,可以估算土壤湿度,监测农作物旱情。重点研究了LST/NDVI特征空间中干湿边的提取方... 农业旱灾是人类面临的最主要自然灾害之一,对我国农业生产影响非常大。土壤含水量是农业干旱监测的重要指标,通过遥感地表温度(LST)与植被指数(NDVI)结合,可以估算土壤湿度,监测农作物旱情。重点研究了LST/NDVI特征空间中干湿边的提取方法,通过14年全国NOAA/AVHRR的8km合成数据集分析发现,在LST/NDVI特征空间中,曲线斜率与实测土壤湿度显著相关(R=0.78,P<0.01),干边的截距和斜率与湿边有比较稳定的关系,但干湿边存在较大的空间和时间变异性。将全国分为6个区,分别确定LST/NDVI特征空间,根据特征空间干湿边参数反演土壤湿度,在土壤湿度分布图和全国耕地分布图基础上,进行旱情分级,得到全国耕地旱情分布图,可以为农业抗旱救灾提供快速宏观的信息服务。 展开更多
关键词 农业干旱 土壤湿度 表面温度 植被指数LST/NDVI特征空间 干湿边
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Accuracy improvement of geometric correction for CHRIS data
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作者 WANG Dian-zhong PANG Yong GUO Zhi-feng 《Journal of Forestry Research》 SCIE CAS CSCD 2008年第3期260-262,共3页
This paper deals with a new type of multi-angle remotely sensed data--CHRIS(the Compact High Resolution Imaging Spec-trometer),by using rational function models(RFM)and rigorous sensor models(RSM).For ortho-rectifying... This paper deals with a new type of multi-angle remotely sensed data--CHRIS(the Compact High Resolution Imaging Spec-trometer),by using rational function models(RFM)and rigorous sensor models(RSM).For ortho-rectifying an image set,a rigorous sen-sor model-Toutin's model was employed and a set of reported parameters including across track angle,along track angle,IFOV,altitude,period,eccentricity and orbit inclination were input,then,the orbit calculation was started and the track information was given to the raw data.The images were ortho-rectified with geocoded ASTER images and digital elevation(DEM)data.Results showed that with 16 ground control points(GCPs),the correction accuracy decreased with view zenith angle,and the RMSE value increased to be over one pixel at 36 degree off-nadir.When the GCPs were approximately chosen as in Toutin's model,a RFM with three coefficients produced the same accuracy trend versus view zenith angle while the RMSEs for all angles were improved and within about one pixel. 展开更多
关键词 CHRIS ortho-rectify rigorous sensor model rational function model
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