AisaFENIX應(yīng)用于土壤重金屬檢測(cè)(引自:SeongJoo Kang etc. Evaluating laboratory-based classification potentials of heavy metal contaminated soils using spectro-radiometer and hyperspectral imagery. Spet. Inf. Res. 2019)
ESA與NASA合作,采用基于AisaIBIS的HyPlant SIF航空遙感系統(tǒng)、美國(guó)NASA研發(fā)的基于LiDAR-高光譜-紅外熱成像航空遙感系統(tǒng),同步獲取森林的太陽(yáng)光誘導(dǎo)葉綠素?zé)晒獬上瘛⒐趯咏Y(jié)構(gòu)信息、可見(jiàn)光至短波紅外(400-2500nm)光譜反射成像信息、及冠層溫度信息,以觀測(cè)研究生態(tài)系統(tǒng)健康與碳循環(huán)動(dòng)態(tài)(Middleton etc. The 2013 FLEX-US airborne campaign at the parker tract loblolly pine plantation in North Carolina, USA. Remote Sensing, 2013)
德國(guó)Julich研究所、西班牙Valencia大學(xué)、意大利Milano-Bicocca大學(xué)、芬蘭Specim公司等科學(xué)家,對(duì)基予AisaIBIS的HyPlant航空遙感系統(tǒng)(包括AisaIBIS和AisaFENIX)觀測(cè)冠層(Top-of-Canopy, TOC)光譜反射與SIF葉綠素?zé)晒饧夹g(shù),進(jìn)行了全面解讀,并采用該系統(tǒng)對(duì)農(nóng)田作物進(jìn)行了遙感作圖分析(參見(jiàn)下圖),該系統(tǒng)采用AisaIBIS、AisaFENIX全波段空陸雙基高光譜成像(400-2500nm)等(Basbian Siegmann etc. The high-performance airborne imaging spectrometer HyPlant-from raw images to Top-of-Canopy reflectance and fluorescence products: Introduction of an Automatized Processing China. Remote Sensing, 2019)
德國(guó)科隆大學(xué)等科學(xué)家采用HyPlant航空遙感系統(tǒng)(基于AisaIBIS SIF葉綠素?zé)晒飧吖庾V成像和AisaFENIX高光譜成像技術(shù)),結(jié)合地面光合作用(采用Li6400或LCPro T光合儀)和土壤呼吸測(cè)量(采用Li8100或SRS2000土壤呼吸測(cè)量系統(tǒng)),對(duì)植被初級(jí)生產(chǎn)力及脅迫進(jìn)行了觀測(cè)研究(參見(jiàn)下圖),結(jié)果表明,F(xiàn)760對(duì)現(xiàn)有GPP評(píng)估方法可以起到很好的改善和補(bǔ)充,SIF紅色葉綠素?zé)晒馀c遠(yuǎn)紅波段葉綠素?zé)晒獗嚷士梢造`敏地反映環(huán)境脅迫(S. Wieneke etc. Airborne based spectroscopy of red and far-red sun-induced chlorophyll fluorescence: Implications for improved estimates of gross primary productivity. Remote Sensing of Environment, 2016)
其它參考文獻(xiàn):
1)Rascher, U., et al.(2015), Sun-induced fluorescenc – a new probe of photosynthesis: First maps from the imaging spectrometer HyPlant. Global Change Biology.
2)Rossini, M., et al.(2015), Red and far red Sun-induced chlorophyll fluorescence as a measure of plant photosynthesis, Geophys. Res. Lett.
3)Wieneke, S., et al.(2016), Airborne based spectroscopy of red and far-red sun-induced chlorophyll fluorescence: Implications for improved estimates of gross primary productivity. Remote Sensing of Environment.
4)Colombo, R., et al.(2018), Variability of sun-induced chlorophyll fluorescence according to stand age-related processes in a managed loblolly pine forest. Global Change Biology.
5)Gerhards, M., et al.(2018), Analysis of airborne optical and thermal imagery for detection of water stress symptoms. Remote Sensing.
6)Max Gerhards, et al.(2018), Analysis of airborne optical and thermal imagery for detection of water stress symptom. Remote Sensing.
7)Bandopadhyay, S., et al. (2018), Examination of Sun-induced Fluorescence (SIF) Signal on Heterogeneous Ecosystem Platforms using ‘HyPlant’. Geophysical Research Abstracts.
8)Giulia Tagliabue, et al. (2019), Exploring the spatial relationship between airborne-derived red and far-red sun-induced fluorescence and process-based GPP estimates in a forest ecosystem. Remote Sensing of Environment.