Sustainable development calls for the wise use of groundwater resources. Of particular concern is saline intrusion into productive agricultural land, which is contiguous with densely populated coastal settlements. To reverse saline intrusion in such coastal regions, information about the groundwater depth in terms of its spatio-temporal variability is essential. Using survey data from 2004 to 2007, the research revealed the temporal variation characteristics of groundwater depth in the Contemporary Yellow River Delta. It explored the temporal stability characteristics of groundwater depth by using the coefficient of variation, Spearman rank correlation coefficient, and average relative deviation and standard deviation, and confirmed that the representative point reflected the average groundwater depth of the study area. Results showed that spatial variation of the groundwater depth in the study area was medium, but the variation coefficient of groundwater depth showed the seasonal changes. The spatial variation coefficient was largest in the dry season; the other months were relatively stable. The groundwater depth in the study area had strong temporal stability. The correlation between the Spearman rank correlation coefficient and the time lags showed that the spatial pattern of groundwater depth in the study area was similar across two or three years but the similarity weakened beyond this period. The representative points of the whole area showed a good linear correlation, and were spatially concentrated. In different years or time periods, the representative points were not the same but belonged to the medium groundwater depth grade in the area. The study provides useful guidance for Yellow River irrigation, preventing saline intrusion and the restoration of saline-alkali soils. It offers a theoretical basis for identifying regional satellite groundwater depth monitoring points.

The ability to accurately measure the geometric characteristics of soil wetted bodies (SWBs) is very important for conserving water in agriculture. However, measurements of SWBs obtained using conventional methods have a number of defects. Ground penetrating radar (GPR) is a promising technique for detecting buried features. In this paper, we used GPR to nondestructively investigate SWBs formed under drip irrigation. First, numerical simulations were performed to obtain the theoretical radar-gram of SWBs. Then, controlled irrigation experiments were performed to obtain radar scan datasets in a laboratory. The GPR image was interpreted according to the numerical simulation results, and the SWB thickness detection accuracy was estimated. Finally, GPR detection was performed in the field with different irrigation volumes, and the detection effect was assessed. The GPR reflections in the laboratory and field measurements were more complex than in the numerical simulation images, but the location and thickness of SWBs were still clear; the accuracy of the measured thickness was high, and the accuracy decreased with an increase in irrigation volume. The radar image resolution and thickness accuracy measured in the field were slightly less than the values measured in the laboratory. Thus, GPR is able to quickly and accurately characterize SWBs formed by drip irrigation based on the thickness and relative position in the soil. Furthermore, the F-K offset transformation was an effective GPR data processing method for focusing reflections from SWBs to obtain their true position and physical extent.

Nondestructive and noninvasive visualization and quantification of soil wetted bodies (SWBs) is of great significance to the development of water-saving agriculture. Unfortunately, measuring the parameters of SWBs is difficult due to the invisibility of SWBs buried underneath the ground and the non-variability of the soil moisture under partial irrigation conditions. Therefore, we performed a laboratory experiment to investigate what SWB attributes can be detected by a GPR. In the laboratory, three typical partial irrigation experiments were conducted to collect the GPR data of SWBs of different sizes, shapes, and burial depths. Additionally, numerical simulation scenarios were designed according to the laboratory experiment. Then, the simulated and measured GPR data were processed by the FK migration method. Based on the simulation, a calibration model for the width of SWBs was constructed. We found that SWB attributes, such as type and location can be obtained from raw radargrams owing to the obvious reflection of the top and bottom interfaces. The results showed that estimating the depth and thickness of SWBs from FK migration radargrams is more reliable than from raw radargrams. Moreover, estimation of the width of SWBs relies on the FK migration radargrams. Our findings indicate that the size and depth of SWBs dominate the width detection accuracy, and the estimated width gained via the width calibration model is improved. Our results highlight the potential for using GPR data to detect SWBs, as well as the potential of using numerical simulation, FK migration, and calibration modeling in combination to extract the size information of SWBs from GPR radargrams.

Solar-induced chlorophyll fluorescence (SIF) can accurately reflect the photosynthetic capacity of vegetation and information on the physiological status of vegetation, which is of great research significance and application value. However, the low resolution of the solar-induced chlorophyll fluorescence product of the inverse performance makes it difficult to perform fine-scale studies. Therefore, concerning the above problem, this study proposes a random-forest-based downscaling method based on random forest. We used MODIS reflectance data to analyze GOME-2 SIF data at 0.5° resolution over the Chinese mainland in 2011 and created a monthly SIF product at 0.05° resolution for the Chinese mainland through downscaling. Then we performed a veracity check on the downscaled SIF data, analyzed factors (land cover type, climate zone type, and DEM) that could affect its accuracy, and explored the feasibility of using MODIS data in future GOME-2 SIF downscaling studies. The results show that the downscaled SIF is in remarkable agreement with the GOME-2 SIF, with an improved spatial resolution from 0.5° to 0.05°, and that the structural and physiological information of the SIF is well represented in the downscaled SIF, which is essential for assessing global photosynthesis. In addition, in the region with grassland land cover type, temperate grassland climate region, alpine vegetation climate region of Qinghai–Tibet Plateau, as well as the region with high altitude and complex terrain, the accuracy of using MODIS to downscale the GOME-2 SIF data is low.

Innovative education has become the theme of higher education in China. Educators should not only cultivate students' innovative ability, but also use effective teaching mode to cultivate students' innovative thinking. In this paper, we selected 138 students in the first grade of a university in a certain province to carry out a half-year experiment of innovative thinking teaching in the computer classroom of a university to observe whether the students' innovative thinking ability has changed or not. After a semester of teaching, the test results before and after the two classes show that the innovative thinking ability of the control class has been improved but not significant, and the students' ability of the experimental class has been improved comprehensively and significantly. The results of this study have certain enlightenment and reference value for the cultivation of innovative thinking teaching mode in Chinese college students.