Improving winter wheat yield estimation by assimilation of the leaf area index from Landsat TM and MODIS data into the WOFOST model

To predict regional-scale winter wheat yield, we developed a crop model and data assimilation framework that assimilated leaf area index (LAI) derived from Landsat TM and MODIS data into the WOFOST crop growth model. We measured LAI during seven phenological phases in two agricultural cities in China’s Hebei Province. To reduce cloud contamination, we applied Savitzky–Golay (S–G) filtering to the MODIS LAI products to obtain a filtered LAI. We then regressed field-measured LAI on Landsat TM vegetation indices to derive multi-temporal TM LAIs. We developed a nonlinear method to adjust LAI by accounting for the scale mismatch between the remotely sensed data and the model’s state variables. The TM LAI and scale-adjusted LAI datasets were assimilated into the WOFOST model to allow evaluation of the yield estimation accuracy. We constructed a four-dimensional variational data assimilation (4DVar) cost function to account for the observations and model errors during key phenological stages. We used the shuffled complex evolution–University of Arizona algorithm to minimize the 4DVar cost function between the remotely sensed and modeled LAI and to optimize two important WOFOST parameters. Finally, we simulated winter wheat yield in a 1-km grid for cells with at least 50% of their area occupied by winter wheat using the optimized WOFOST, and aggregated the results at a regional scale. The scale adjustment substantially improved the accuracy of regional wheat yield predictions (R2 = 0.48; RMSE= 151.92 kg ha−1) compared with the unassimilated results (R2 = 0.23;RMSE= 373.6 kg ha−1) and the TM LAI results (R2 = 0.27; RMSE= 191.6 kg ha−1). Thus, the assimilation performance depends strongly on the LAI retrieval accuracy and the scaling correction. Our research provides a scheme to employ remotely sensed data, ground-measured data, and a crop growth model to improve regional crop yield estimates

Improving winter wheat yield estimation by assimilation of the leaf area index from Landsat TM and MODIS data into the WOFOST model

To predict regional-scale winter wheat yield, we developed a crop model and data assimilation framework that assimilated leaf area index (LAI) derived from Landsat TM and MODIS data into the WOFOST crop growth model. We measured LAI during seven phenological phases in two agricultural cities in China’s Hebei Province. To reduce cloud contamination, we applied Savitzky–Golay (S–G) filtering to the MODIS LAI products to obtain a filtered LAI. We then regressed field-measured LAI on Landsat TM vegetation indices to derive multi-temporal TM LAIs. We developed a nonlinear method to adjust LAI by accounting for the scale mismatch between the remotely sensed data and the model’s state variables. The TM LAI and scale-adjusted LAI datasets were assimilated into the WOFOST model to allow evaluation of the yield estimation accuracy. We constructed a four-dimensional variational data assimilation (4DVar) cost function to account for the observations and model errors during key phenological stages. We used the shuffled complex evolution–University of Arizona algorithm to minimize the 4DVar cost function between the remotely sensed and modeled LAI and to optimize two important WOFOST parameters. Finally, we simulated winter wheat yield in a 1-km grid for cells with at least 50% of their area occupied by winter wheat using the optimized WOFOST, and aggregated the results at a regional scale. The scale adjustment substantially improved the accuracy of regional wheat yield predictions (R2 = 0.48; RMSE= 151.92 kg ha−1) compared with the unassimilated results (R2 = 0.23;RMSE= 373.6 kg ha−1) and the TM LAI results (R2 = 0.27; RMSE= 191.6 kg ha−1). Thus, the assimilation performance depends strongly on the LAI retrieval accuracy and the scaling correction. Our research provides a scheme to employ remotely sensed data, ground-measured data, and a crop growth model to improve regional crop yield estimates

Spatiotemporal vortex strings of light

Light carrying orbital angular momentum (OAM) holds unique properties and boosts myriad applications in diverse fields from micro- to macro-world. Endeavors have been made to manipulate the OAM in order to generate on-demand structured light and to explore novel properties of light. However, the generation of an ultrafast wave packet carrying numerous vortices with various OAM modes, that is vortex string, has been rarely explored and remains a significant challenge. Moreover, methods that enable parallel detection of all vortices in a vortex string are lacking. Here, we demonstrate that a vortex string with 28 spatiotemporal optical vortices (STOVs) can be successfully generated in an ultrafast wave packet. All STOVs in the string can be randomly or orderly arranged. The diffraction rules of STOV strings are also revealed theoretically and experimentally. Following these rules, the topological charges and positions of all STOVs in a vortex string can be easily recognized. The strategy for parallel generation and detection of STOV strings will open up exciting perspectives in STOV-based optical communications and also promote promising applications of the structured light in light-matter interaction, quantum information processing, etc

Prospect of China's Auroral Fine-structure Imaging System (CAFIS) at Zhongshan station in Antarctica

A new auroral imaging system is reported which is planned to be deployed at Zhongshan Station in Antarctica in the end of 2009. The system will focus on study of optical auroras in small scales and be called China's Auroral Fine-structure Imaging System (CAFIS). The project of CAFIS is carried out by support of the tenth five-year plan for capacity building of China. CAFIS will be a powerful ground-based platform for aurora observational experiments. Composing and advantages of CAFIS are introduced in this brief report. Some potential study topics involved CAFIS are also considered

Adsorption capacity and thermal effect of adsorbents for oil vapor with humidity

As the standards for emissions of organic waste gas become increasingly strict, the competitive adsorption of water vapor and organic waste gas is becoming the hot spot of research on treatment of organic waste gas. Therefore, three adsorbents (AdsFOV-1, AdsFOV-2 and AdsFOV-3) were used for the research, and their adsorption capacity and thermal effect were investigated through the static adsorption to the vapors of n-hexane, gasoline and water after structural characterization. In addition, dynamic adsorption experiments were performed with the vapors of n-hexane and gasoline under different relative humidity conditions, so as to study the influence of relative humidity on adsorption effect and temperature rising curve, as well as the stability of cyclic adsorption. Meanwhile, the kinetics behavior of the adsorption process was judged by kinetic fitting. The results show that: the adsorption capacity of the three adsorbents to the vapors of n-hexane and gasoline decreases with the increasing of relative humidity, but the thermal effect of adsorption becomes more obvious. Besides, AdsFOV-1 and AdsFOV-2 have good cyclic adsorption performances, specifically, when the inlet vapor mass concentration is set to 25 g/m3, the outlet vapor mass concentration of the adsorption tower can meet the emission requirements of less than 80 mg/m3. Further, the adsorption kinetics behavior of the three adsorbents on the vapor of n-hexane is consistent with the Bangham dynamic equation, which means the adsorption rate is mainly controlled by the pore diffusion. In general, the research results could provide theoretical basis for the treatment of industrial organic waste gas with high humidity

Multi-decadal analysis of high-resolution albedo changes induced by urbanization over contrasted Chinese cities based on Landsat data

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Blue laser diode pumped Pr:YLF green laser

Blue laser diode pumped Pr:YLF solid-state green laser is reported. A 5 mm long Pr:YLF crystal with the doping concentration of 0.5% is used as laser gain material, pumped by a blue laser diode with emitting central wavelength at 444 nm. Continuous-wave green laser output at 522.4 nm is obtained. Input and output characteristics of the lasers are studied with different output couplers. Under an absorbed pump power of about 530 mW, the maximum output power of 90.1 mW is obtained with 1.9% transmission coupler. The slope efficiency is as high as 65.3%

Blue laser diode pumped Pr3+: YLF visible lasers

Red (640 nm) and green (522 nm) lasers with Pr3+: YLF pumped by a blue laser diode emitting at about 444 nm in the longitudinal direction are reported. On the basis of the beam reshaping of the pump spot by a prism, the maximum output power, absorbed threshold pump power and slope efficiency of the red laser are 308.5 mW, 46 mW and 47.5%, respectively. The three corresponding parameter values for green laser are 193.4 mW, 162.3 mW and 37.1%, respectively. The results show that the shaped pump beam is beneficial to improve the output characteristics of these two visible laser emissions

Continuous-Wave Ultraviolet Generation at 349 nm by Intracavity Frequency Doubling of a Diode-Pumped Pr:LiYF4 Laser

National Natural Science Foundation of China [61275050]; Specialized Research Fund for the Doctoral Program of Higher Education [20120121110034]; Xiamen Scientific and Technologic Project [3502Z20113004]We report a continuous-wave ultraviolet laser at 349 nm obtained by intracavity frequency doubling of a diode-pumped Pr3+-doped LiYF4 laser. Power scaling of lasers at 698-nm fundamental wavelength was realized using an InGaN laser diode with a maximum output power of 850 mW and a 5-mm-long 0.5 a.t. % laser crystal. The maximum output power at 698 nm was 215 mW. With beta barium borate crystal employed as the nonlinear medium, 33 mW of output power at 349 nm has been achieved