The ratio of leaf carotenoid to chlorophyll(Car/Chl)is an indicator of vegetation photosynthesis,development and responses to stress.However,the correlation between Car and Chl,and their overlapping absorption in the ...The ratio of leaf carotenoid to chlorophyll(Car/Chl)is an indicator of vegetation photosynthesis,development and responses to stress.However,the correlation between Car and Chl,and their overlapping absorption in the visible spectral domain pose a challenge for optical remote sensing of their ratio.This study aims to investigate combinations of vegetation indices(VIs)to minimize the influence of Car-Chl correlation,thus being more sensitive to the variability in the ratio across vegetation species and sites.VIs sensitive to Car and Chl variability were combined into four candidates of combinations,using a simulated dataset from the PROSPECT model.The VI combinations were then tested using six simulated datasets with different Car-Chl correlations,and evaluated against four independent datasets.The ratio of the carotenoid triangle ratio index(CTRI)with the red-edge chlorophyll index(CIred-edge)was found least influenced by the Car-Chl correlation and demonstrated a superior ability for estimating Car/Chl variability.Compared with published VIs and two machine learning algorithms,CTRI/CIred-edge also showed the optimal performance in the fourfield datasets.This new VI combination could be useful to provide insights in spatiotemporal variability in the leaf Car/Chl ratio,applicable for assessing vegetation physiology,phenology,and response to environmental stress.展开更多
水稻冠层叶片含水量(leaf water content,LWC)快速无损监测对指导稻田精准灌溉和提高水稻水分利用效率具有重要意义。试验设置3个不同水分处理(传统淹灌、轻度干湿交替-15 kPa、重度干湿交替-30 kPa),于水分敏感期(抽穗灌浆期)动态监测...水稻冠层叶片含水量(leaf water content,LWC)快速无损监测对指导稻田精准灌溉和提高水稻水分利用效率具有重要意义。试验设置3个不同水分处理(传统淹灌、轻度干湿交替-15 kPa、重度干湿交替-30 kPa),于水分敏感期(抽穗灌浆期)动态监测顶1叶(L_(1))、顶2叶(L_(2))和顶3叶(L_(3))的光谱数据和叶绿素荧光参数,通过全光谱波段筛选出水分敏感波段,建立新型植被指数,结合叶绿素荧光参数,以期建立基于叶位组合的水稻冠层LWC精准监测模型。结果表明:水稻叶片水分敏感波段在近红外波段(1000~1400 nm),所构建新型植被指数NDSII_((1114,1387))较传统植被指数能更好地监测LWC;通过筛选与LWC有高相关性的荧光参数,基于实际光量子产量Y(Ⅱ)和植被指数NDSII_((1114,1387))的耦合监测模型较单一植被指数NDSII_((1114,1387))模型精度提高71.807%~83.976%。与单叶相比,L_(2)和L_(3)叶位组合的Y(Ⅱ)和植被指数NDSII_((1114,1387))耦合模型对水稻冠层LWC监测精度相较L_(2)、L_(3)分别显著(P<0.05)提高11.641%和23.029%。由此表明,基于叶位组合的叶片反射光谱与叶绿素荧光耦合可有效监测水稻冠层LWC,为光学仪器监测水稻LWC提供理论基础,并对未来利用反射光谱与荧光参数进行作物光合作用研究提供理论支持。展开更多
基金supported by the National Natural Science Foundation of China(42001314)the Open Research Fund of the State Laboratory of Information Engineering in Surveying,Mapping,and Remote Sensing,Wuhan University(grant number 20R02)+1 种基金Torbern Tagesson was additionally funded by the Swedish National Space Agency(SNSA 2021-00144)FORMAS(Dnr.2021-00644).
文摘The ratio of leaf carotenoid to chlorophyll(Car/Chl)is an indicator of vegetation photosynthesis,development and responses to stress.However,the correlation between Car and Chl,and their overlapping absorption in the visible spectral domain pose a challenge for optical remote sensing of their ratio.This study aims to investigate combinations of vegetation indices(VIs)to minimize the influence of Car-Chl correlation,thus being more sensitive to the variability in the ratio across vegetation species and sites.VIs sensitive to Car and Chl variability were combined into four candidates of combinations,using a simulated dataset from the PROSPECT model.The VI combinations were then tested using six simulated datasets with different Car-Chl correlations,and evaluated against four independent datasets.The ratio of the carotenoid triangle ratio index(CTRI)with the red-edge chlorophyll index(CIred-edge)was found least influenced by the Car-Chl correlation and demonstrated a superior ability for estimating Car/Chl variability.Compared with published VIs and two machine learning algorithms,CTRI/CIred-edge also showed the optimal performance in the fourfield datasets.This new VI combination could be useful to provide insights in spatiotemporal variability in the leaf Car/Chl ratio,applicable for assessing vegetation physiology,phenology,and response to environmental stress.
