ALBERT

Description: The distribution of corn cultivation areas is crucial for ensuring food security, eradicating hunger, adjusting crop structures, and managing water resources. The emergence of high-resolution images, such as Sentinel-1 and Sentinel-2, enables the identification of corn at the field scale, and these images can be applied on a large scale with the support of cloud computing technology. Hebei Province is the major production area of corn in China, and faces serious groundwater overexploitation due to irrigation. Corn was mapped using multitemporal synthetic aperture radar (SAR) and optical images in the Google Earth Engine (GEE) cloud platform. A total of 1712 scenes of Sentinel-2 data and 206 scenes of Sentinel-1 data acquired from June to October 2017 were processed to composite image metrics as input to a random forest (RF) classifier. To avoid speckle noise in the classification results, the pixel-based classification result was integrated with the object segmentation boundary completed in eCognition software to generate an object-based corn map according to crop intensity. The results indicated that the approach using multitemporal SAR and optical images in the GEE cloud platform is reliable for corn mapping. The corn map had a high F1-Score of 90.08% and overall accuracy of 89.89% according to the test dataset, which was not involved in model training. The corn area estimated from optical and SAR images was well correlated with the census data, with an R2 = 0.91 and a root mean square error (RMSE) of 470.90 km2. The results of the corn map are expected to provide detailed information for optimizing crop structure and water management, which are critical issues in this region.

Description: Although body-centered cubic (BCC) structural magnesium–lithium (Mg-Li) alloys have lower density and better formability than common hexagonal close-packed (HCP) Mg alloys, their applications remain limited due to their low strength. The purpose of this study is to investigate the effect of Y/Er and Zn addition on the microstructure and tensile properties of Mg-11Li alloy with a BCC structural matrix by comparing Mg-11Li, Mg-11Li-4Y-2Er-2Zn, and Mg-11Li-8Y-4Er-4Zn (wt %) alloys. The results indicate that the addition of Y/Er and Zn at a ratio of 3:1 cannot promote the formation of long-period stacking ordered structure in Mg-11Li alloy such as that in Mg-Y-Er-Zn alloys and the dominant intermetallic phases formed are BCC Mg24RE5 and face-centered cubic (FCC) Mg3RE2Zn3 phases. With an increase of the content of Y/Er and Zn in an as-cast alloy, the fraction of intermetallic particles increases and the grain size decreases. The addition of Y/Er, as well as Zn, dramatically promotes the refinement of dynamic recrystallization (DRX) during extrusion. The initial intermetallic phases induced by Y/Er and Zn addition are broken into relatively fine particles during extrusion, and this contributes to refining the dynamic recrystallized (DRXed) grains mainly by the particle stimulated nucleation mechanism. The as-extruded Mg-11Li-4Y-2Er-2Zn and Mg-11Li-8Y-4Er-4Zn alloys exhibit much higher tensile strength as compared with as-extruded Mg-11Li alloy, which is mainly ascribed to the refined DRXed grains and numerous dispersed intermetallic phase particles. It is suggested that further refinement of intermetallic particles in these extruded Mg-11Li-based alloys may lead to higher quality alloy materials with low density and excellent mechanical properties.

Description: In Mongolia, the monitoring and estimation of spring wheat yield at the regional and national levels are key issues for the agricultural policy and food management as well as for the economy and society as a whole. The remote sensing data and technique have been widely used for the estimation of crop yield and production in the world. For the current research, nine remote sensing indices were tested that include normalized difference drought index (NDDI), normalized difference water index (NDWI), vegetation condition index (VCI), temperature condition index (TCI), vegetation health index (VHI), normalized multi-band drought index (NMDI), visible and shortwave infrared drought index (VSDI), and vegetation supply water index (VSWI). These nine indices derived from MODIS/Terra satellite have so far not been used for crop yield prediction in Mongolia. The primary objective of this study was to determine the best remote sensing indices in order to develop an estimation model for spring wheat yield using correlation and regression method. The spring wheat yield data from the ground measurements of eight meteorological stations in Darkhan and Selenge provinces from 2000 to 2017 have been used. The data were collected during the period of the growing season (June–August). Based on the analysis, we constructed six models for spring wheat yield estimation. The results showed that the range of the root-mean-square error (RMSE) values of estimated spring wheat yield was between 4.1 (100 kg ha−1) to 4.8 (100 kg ha−1), respectively. The range of the mean absolute error (MAE) values was between 3.3 to 3.8 and the index of agreement (d) values was between 0.74 to 0.84, respectively. The conclusion was that the best model would be (R2 = 0.55) based on NDWI, VSDI, and NDVI out of the nine indices and could serve as the most effective predictor and reliable remote sensing indices for monitoring the spring wheat yield in the northern part of Mongolia. Our results showed that the best timing of yield prediction for spring wheat was around the end of June and the beginning of July, which is the flowering stage of spring wheat in this study area. This means an accurate yield prediction for spring wheat can be achieved two months before the harvest time using the regression model.