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Continuous and automatic monitoring of canopy phenology is of increasing scientific interest as a tool to investigate the multiple implications of vegetation dynamics on ecosystem carbon fluxes.For this purpose,we initiated research on winter wheat ecosystem northern China in 2011,belonging to Chinese Terrestrial Ecosystem Flux Observation and Research Network(ChinaFLUX),and evaluated the applicability of digital camera imagery for monitoring and modeling crop phenology and physiology.A standard,commercially available webcam was mounted at the top of the eddy covariance tower at the YuCheng ChinaFLUX site.Images were collected in growing season every 30 min from 9:30 a.m to 5:00 p.m each day.Red,green,and blue color channel brightness data for a region-of-interest(ROI) were extracted from each image(ROI is the subset of image,can better describe the target′s characters.The size of ROI for winter wheat is 380*260 pixels).Since a temporal series of original brightness data can′t capture the changes at different canopy development stages,we compared different indices(ratio greenness index,excess greenness index,and relative greenness index) composed of different channel brightness data.Ratio greenness index(G/R),can reflect the size of leaf area index and variations in chlorophyll content of winter wheat,proved to be the index best describing the green-up signals of the vegetation,was calculated by dividing green channel brightness by red channel brightness,and extracted from chronically digital images.Phenological date was defined as the date on which the curvature of G/R reaches its peak and compared to field-measured phenological date.The results confirmed that G/R was useful to monitor canopy phenology.We further investigated the relationship between G/R and carbon exchange capacity(i.e.,gross ecosystem exchange ) of vegetation using eddy covariance CO2 flux data.A strong relationship(GEE: r2 = 0.66) between ratio greenness and photosynthesis was observed for winter wheat.At the same time,we used the daily NEE and PAR data collected from Eddy covariance technique,employed the Michaelis-Menten equationt,and obtained the curve of Amax.The result demonstrated a positive relationship between G/R and Amax,and suggested that G/R is able to reflect the plant phenological activity in physiological level.We concluded that digital camera images not only provide a reliable measure of plant phenology at high tempo-spatial resolutions,but also act as a complementary role of CO2 flux measurements,and improve our knowledge of ecosystem processes.Digital cameras have been installed at eight typical terrestrial ecosystem sites of ChinaFLUX for phenological observations,constituting of the Chinese Digital Camera Phenology Observation Network.This platform will provide an unprecedented opportunity to obtain an improved understanding of vegetation responses to climate change in China.With high tempo-spatial resolution phenological data,we can bring insights into the process of biologically mediated carbon sources and sinks,and better understand the uncertainties of the role of terrestrial ecosystems in the global carbon cycle.

As one of the most widespread forest types,China′s plantation plays an important role in global carbon balance.It is crucial to reduce the uncertainties in the estimation of carbon and water fluxes of plantation ecosystems,and model-data fusion technique provides an effective way.The purpose of this research is to improve the modeling accuracy of SIPNET model,the simplified Photosynthesis and Evapo-Transpiration(ET) model through two experiments,namely NEE alone and NEE ET multi-constraints.The model-data fusion method used here is a combination of Metropolis-Hastings algorithm and Simulated Annealing algorithm.Based on eddy fluxes and meteorological observation data of Qianyanzhou subtropical coniferous plantation during 2004—2009 in ChinaFLUX(Chinese Terrestrial Ecosystem Flux Research Network),we estimated the key parameters of SIPNET model and simulated the corresponding carbon and water fluxes.Comparisons between the measured and modeled net ecosystem exchange of carbon dioxide(NEE) showed that the SIPNET model had approximately equivalent fits to the observed NEE under two optimization procedures(R2 decreased from 0.934 to 0.929,and RMSE increased from 0.736 g C/m2 to 0.763 g C/m2).In the case of ET,the NEE and ET parameterization produced a markedly better fit to the observed ET than the NEE parameterization(R2 increased from 0.188 to 0.824,and RMSE decreased from 0.152 cm to 0.053 cm).As for transpiration,when optimized by observed NEE alone,SIPNET largely underestimated annual accumulated transpiration in 2004 compared with the measurements of sap flow technique.In comparison,while optimization based on NEE and ET,SIPNET led to a better fit of annual cumulative estimation of transpiration in 2004 to the sap flow measurement.These results indicated that the SIPNET model parameterized using NEE and ET observed fluxes could well reproduce the characteristics of carbon and water fluxes.In other words,more information can be extracted from simultaneous optimization,since there is additional process information in water flux observation data.Furthermore,we conducted a sensitivity test of precipitation on carbon fluxes through reduction of precipitation.We found that photosynthesis was more sensitive to precipitation reduction than respiration,and the model optimized using NEE and ET reproduced the response of NEE to precipitation reduction better than that optimized using NEE alone.In addition,we detected that the difference of NEE response to precipitation reduction in two optimizations of the SIPNET model was caused by gross ecosystem production rather than ecosystem respiration.Therefore,parameter estimation using NEE and ET altogether improved the performance of SIPNET model.And without optimization using both NEE and ET,the response of ecosystem carbon cycle to precipitation variation may be misrepresented.

In order to study the effects of laser shock peening on the fatigue life of LY2 aluminum alloy, water and aluminum foil were used for confining medium and ablation medium, respectively. LY2 specimens were processed with YAG laser. The laser peening parameters were: wavelength of 532 nm, pulse duration of 10 ns, and output energy of 5 J. The laser was focused on a spot of diameter 3 mm. The experimental results show that laser shock peening can increase aluminum fatigue life by 110%. Fracture analysis results show that laser shock peening strengthens the metal surface and suppresses the crack’s initiation and expansion, improving the aluminum fatigue performance．

The high-energy Nd: YAG laser YLSS-M60U was adopted for laser shock processing on K403/K3 cast superalloy test specimen from pressure-turbine blade materials of a certain type of engine. The processing parameters are: laser energy 3 J, spot diameter 2.6 mm, laser pulse 20 ns, laser wave length 1064 nm. An aluminium foil was used as the protection coating, along with water as the restraint layer, the overlap rate reached 50% after 3 times of shocking. The high-cycle fatigue (HCF) test was conducted under the room temperature at stress level of 420 MPa as well as the SEM observation and XRD (x-Ray diffraction) analysis after laser shock processing. The study shows that the leading reasons for improving the fatigue property of metal involve that the fatigue life of the test specimen was 2.4 times longer than the original after laser shock processing, the strong shock waves plastically deform the metal with high-strain rate and generate much larger and deeper residual compressive stress. (authors)