Remote Sensing Detection of Wheat Stripe Rust by Synergized Solar-Induced Chlorophyll Fluorescence and Differential Spectral Index

2019 ◽  
Author(s):  
Xia Jing ◽  
Zongfan Bai
Keyword(s):  
Remote Sensing ◽  
Chlorophyll Fluorescence ◽  
Spectral Index ◽  
Stripe Rust ◽  
Wheat Stripe Rust

Characterization of Wheat Stripe Rust Resistance Genes in Shaanmai 139

2010 ◽  
Vol 36 (1) ◽  
pp. 109-114 ◽  
Author(s):  
Hong ZHANG ◽  
Zhi-Long REN ◽  
Yin-Gang HU ◽  
Chang-You WANG ◽  
Wan-Quan JI
Keyword(s):  
Stripe Rust ◽  
Resistance Genes ◽  
Rust Resistance ◽  
Stripe Rust Resistance ◽  
Wheat Stripe Rust ◽  
Rust Resistance Genes

scholarly journals Temporal Variation of Chlorophyll-a Concentrations in Highly Dynamic Waters from Unattended Sensors and Remote Sensing Observations

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2699 ◽  
Author(s):  
Jian Li ◽  
Liqiao Tian ◽  
Qingjun Song ◽  
Zhaohua Sun ◽  
Hongjing Yu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Chlorophyll Fluorescence ◽  
Chlorophyll A ◽  
Temporal Variations ◽  
Lake Area ◽  
Short Term ◽  
Chl A ◽  
Spatio Temporal

Monitoring of water quality changes in highly dynamic inland lakes is frequently impeded by insufficient spatial and temporal coverage, for both field surveys and remote sensing methods. To track short-term variations of chlorophyll fluorescence and chlorophyll-a concentrations in Poyang Lake, the largest freshwater lake in China, high-frequency, in-situ, measurements were collected from two fixed stations. The K-mean clustering method was also applied to identify clusters with similar spatio-temporal variations, using remote sensing Chl-a data products from the MERIS satellite, taken from 2003 to 2012. Four lake area classes were obtained with distinct spatio-temporal patterns, two of which were selected for in situ measurement. Distinct daily periodic variations were observed, with peaks at approximately 3:00 PM and troughs at night or early morning. Short-term variations of chlorophyll fluorescence and Chl-a levels were revealed, with a maximum intra-diurnal ratio of 5.1 and inter-diurnal ratio of 7.4, respectively. Using geostatistical analysis, the temporal range of chlorophyll fluorescence and corresponding Chl-a variations was determined to be 9.6 h, which indicates that there is a temporal discrepancy between Chl-a variations and the sampling frequency of current satellite missions. An analysis of the optimal sampling strategies demonstrated that the influence of the sampling time on the mean Chl-a concentrations observed was higher than 25%, and the uncertainty of any single Terra/MODIS or Aqua/MODIS observation was approximately 15%. Therefore, sampling twice a day is essential to resolve Chl-a variations with a bias level of 10% or less. The results highlight short-term variations of critical water quality parameters in freshwater, and they help identify specific design requirements for geostationary earth observation missions, so that they can better address the challenges of monitoring complex coastal and inland environments around the world.

scholarly journals SIFSpec: Measuring Solar-Induced Chlorophyll Fluorescence Observations for Remote Sensing of Photosynthesis

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 3009 ◽  
Author(s):  
Shanshan Du ◽  
Liangyun Liu ◽  
Xinjie Liu ◽  
Jian Guo ◽  
Jiaochan Hu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Chlorophyll Fluorescence ◽  
Seasonal Variations ◽  
Photosynthetic Activity ◽  
Gross Primary Production ◽  
Atmospheric Correction ◽  
Gansu Province ◽  
Optical Measurements ◽  
Calibration Data ◽  
Flux Measurements

Solar-induced chlorophyll fluorescence (SIF) is regarded as a proxy for photosynthesis in terrestrial vegetation. Tower-based long-term observations of SIF are very important for gaining further insight into the ecosystem-specific seasonal dynamics of photosynthetic activity, including gross primary production (GPP). Here, we present the design and operation of the tower-based automated SIF measurement (SIFSpec) system. This system was developed with the aim of obtaining synchronous SIF observations and flux measurements across different terrestrial ecosystems, as well as to validate the increasing number of satellite SIF products using in situ measurements. Details of the system components, instrument installation, calibration, data collection, and processing are introduced. Atmospheric correction is also included in the data processing chain, which is important, but usually ignored for tower-based SIF measurements. Continuous measurements made across two growing cycles over maize at a Daman (DM) flux site (in Gansu province, China) demonstrate the reliable performance of SIF as an indicator for tracking the diurnal variations in photosynthetically active radiation (PAR) and seasonal variations in GPP. For the O2–A band in particular, a high correlation coefficient value of 0.81 is found between the SIF and seasonal variations of GPP. It is thus concluded that, in coordination with continuous eddy covariance (EC) flux measurements, automated and continuous SIF observations can provide a reliable approach for understanding the photosynthetic activity of the terrestrial ecosystem, and are also able to bridge the link between ground-based optical measurements and airborne or satellite remote sensing data.

scholarly journals The barley immune receptor Mla recognizes multiple pathogens and contributes to host range dynamics

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Jan Bettgenhaeuser ◽  
Inmaculada Hernández-Pinzón ◽  
Andrew M. Dawson ◽  
Matthew Gardiner ◽  
Phon Green ◽  
...  
Keyword(s):  
Powdery Mildew ◽  
Stripe Rust ◽  
Resistance Genes ◽  
Host Species ◽  
Plant Pathogens ◽  
Puccinia Striiformis ◽  
Species Specificity ◽  
Wheat Stripe Rust ◽  
Barley Powdery Mildew ◽  
Barley Stripe Rust

AbstractCrop losses caused by plant pathogens are a primary threat to stable food production. Stripe rust (Puccinia striiformis) is a fungal pathogen of cereal crops that causes significant, persistent yield loss. Stripe rust exhibits host species specificity, with lineages that have adapted to infect wheat and barley. While wheat stripe rust and barley stripe rust are commonly restricted to their corresponding hosts, the genes underlying this host specificity remain unknown. Here, we show that three resistance genes, Rps6, Rps7, and Rps8, contribute to immunity in barley to wheat stripe rust. Rps7 cosegregates with barley powdery mildew resistance at the Mla locus. Using transgenic complementation of different Mla alleles, we confirm allele-specific recognition of wheat stripe rust by Mla. Our results show that major resistance genes contribute to the host species specificity of wheat stripe rust on barley and that a shared genetic architecture underlies resistance to the adapted pathogen barley powdery mildew and non-adapted pathogen wheat stripe rust.

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