High-quality ground observation networks are an important basis for scientific research. Here, an automatic soil observation network for high-resolution satellite applications in China (SONTE-China) was established to measure both pixel- and multilayer-based soil moisture and temperature. SONTE-China is distributed across 17 field observation stations with a variety of ecosystems, covering both dry and wet zones. In this paper, the average root mean squared error (RMSE) of station-based soil moisture for well-characterized SONTE-China sites is 0.027 m³/m³ (0.014~0.057 m³/m³) following calibration for specific soil properties. The temporal and spatial characteristics of the observed soil moisture and temperature in SONTE-China conform to the geographical location, seasonality and rainfall of each station. The time series Sentinel-1 C-band radar signal and soil moisture show strong correlations, and the RMSE of the estimated soil moisture from radar data was lower than 0.05 m³/m³ for the Guyuan and Minqin stations. SONTE-China is a soil moisture retrieval algorithm that can validate soil moisture products and provide basic data for weather forecasting, flood forecasting, agricultural drought monitoring and water resource management.

Butyric acid as a renewable resource has become an increasingly attractive alternative to petroleum-based fuels. Clostridium tyrobutyricum ATCC 25755T is well documented as a fermentation strain for the production of acids. However, it has been reported that butyrate inhibits its growth, and the accumulation of acetate also inhibits biomass synthesis, making production of butyric acid from conventional fermentation processes economically challenging. The present study aimed to identify whether irradiation of C. tyrobutyricum cells makes them more tolerant to butyric acid inhibition and increases the production of butyrate compared with wild type.

Remote sensing (RS)-derived vegetation indices (VIs) with medium and high spatial resolution have emerged as a promising dataset for fine-scale ecosystem modeling and agricultural monitoring at local or global scales. Before they can be used as reliable inputs for other research, conducting in situ measurements for validation is very critical. However, the spatial heterogeneity due to the diversity of land cover and its spatial organization in the landscape increases the uncertainty of validation, so design of optimal sampling is an important basis for the reliability of the validation. In this paper, we propose an integrative stratified sampling strategy (INTEG-STRAT) based on normalized difference vegetation index (NDVI) data as prior knowledge. The basic idea is to realize a sampling optimization by determining the optimal combination of the spatial sampling method (e.g., simple random sampling (SRS), spatial system sampling (SYS), stratified sampling, generalized random tessellation stratified (GRTS), balanced acceptance sampling (BAS)) and spatial stratification scheme with an objective rule. The objective rule in this paper is to minimize the root mean square error (RMSE) of 10-fold cross validation between estimated values (sample are not included) and the corresponding values on prior knowledge. Relative precision, correlation coefficient, and RMSE are used to compare the effectiveness of the proposed sampling strategy with each sampling method without considering sampling optimization. After comparing, we find that the INTEG-STRAT requires fewer samples to become stable and has higher accuracy. At site 1, when the correlation coefficient between NDVI image and the simulated NDVI surface reached 80%, INTEG-STRAT needed only 70 sampling points while other methods require more sampling points. At the same time, INTEG-STRAT strategy has a smaller RMSE between the estimated values and the corresponding values on prior knowledge image. In general, INTEG-STRAT is an effective method in the selection of representative samples to support the validation of vegetation indices products with medium and high spatial resolution.

SBA-15 mesoporous molecular sieves modified with copper (Cu-SBA-15) were prepared by pH-adjusting hydrothermal method and characterized by X-ray diffraction, BET, transmission electron microscopy, UV–Vis and ²⁹Si MAS NMR. The pH of the synthesis gel has a significant effect on the amount and the dispersion of copper on SBA-15. The Cu-SBA-15(4.5) (where 4.5 denotes the pH value of the synthesis gel) modified with highly dispersed copper was used as catalyst for the oxidation of aniline by H₂O₂. The Cu-SBA-15(4.5) shows a higher catalytic activity compared to CuO on the surface of SBA-15. The influences of reaction conditions, such as initial pH of the aqueous solutions, temperature, as well as the dosages of H₂O₂ and catalyst were investigated. Under weakly alkaline aqueous solution conditions, the aniline conversion, the H₂O₂ decomposition and the total organic carbon (TOC) removal could be increased significantly compared to the acid conditions. The percentage of leaching Cu²⁺ could be decreased from 45.0% to 3.66% when the initial pH of solution was increased from 5 to 10. The TOC removal could be enhanced with the increases of temperature, H₂O₂ and catalyst dosage, but the aniline conversion and H₂O₂ decomposition change slightly with further increasing dosage of catalyst and H₂O₂. At 343 K and pH 8.0, 100% aniline conversion and 66.9% TOC removal can be achieved under the conditions of 1.0 g/L catalyst and 0.05 mol/L H₂O₂ after 180 min. Although copper might be slightly leached from catalyst, the homogeneous Cu²⁺ contribution to the whole catalytic activity is unimportant, and the highly dispersed copper on SBA-15 plays a dominant role.