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• Dynamic yield prediction with the integral regression model at any time intervals. • Remote sensing-based TVDI replaces precipitation to represent the actual amount of water available to crops. • We couple the three types of factors (sunshine hours, temperature, and precipitation/TVDI) into a comprehensive factor for yield prediction. • The combination of sunshine hours, temperature, and TVDI has better dynamic yield prediction results. This paper describes integrating remote sensing and meteorological data for dynamic winter wheat yield prediction at fixed date intervals with an integral regression model. In most cases, both rainfall and irrigation supply the water needs of crops. We postulated that meteorological precipitation could not fully represent the actual amount of water available to crops, whereas the temperature vegetation dryness index (TVDI) can represent actual water, which monitors soil moisture. According to this, meteorological and remote-sensing data based on sunshine hours, temperature, and TVDI formed a light–temperature–water data combination, which are the three most fundamental and critical factors related to crop growth. The new data combination was used as the input variables of the integral regression model, which could predict the crop yield at a fixed date interval of any length. The dynamic results were then compared with that of the meteorology data combination (sunshine hours, temperature, and precipitation). We selected a total of seven county-level areas in semi-humid and arid regions in China. The proposed dynamic prediction approach achieved good results at the county level from March 21 to maturity at 16-day intervals with predicted R2 > 0.94 and root mean squared error range of 15.54–121.01 kg ha−1 between predicted and actual yield with leave-one-year-out cross-validation for each prediction date. The trend of dynamic prediction accuracy had a relatively stable state. In addition, TVDI contained more crop water information, and prediction results using TVDI and meteorology were generally superior to that using only meteorology for semi-humid and arid areas, with the best results for arid regions. Thus, integrating meteorology and TVDI could effectively dynamically predict yield at county level for most agroclimatic regions, and particularly for arid regions. The work has implications for dynamic yield prediction and improving prediction accuracy and can be extended to other areas. [ABSTRACT FROM AUTHOR]

Amino acids in rain samples offer important information on their origin and roles in biogeochemical cycling. This paper describes the development and validation of an alternative analytical procedure for amino acids in atmospheric precipitation using gas chromatography coupled with mass spectrometry (GC–MS). The method detection limits (MDLs) of these amino acids ranged from 1.46 ng L−1 (Proline) to 135 ng L−1 (Arginine), which were much lower than previous methods like high-performance liquid chromatography (HPLC) and comparable to those with liquid chromatography-tandem mass spectrometry (LC-MS/MS). None of the target analytes were detected in blank samples, which was attributed to the optimized pretreatment protocol we adopted. We conclude that the GC–MS based technique developed in this study possesses advantage due to low detection limits. We further demonstrate the applicability of this method by analyzing rain samples from the background atmosphere of the Tibetan Plateau (TP). Similar relative abundances of amino acids were found in the TP rains, marine rains and Arctic ice. The correlations between FAAs and water- soluble ions in the Tibetan Plateau rainwaters showed that the FAAs were most likely originated from biomass burning. Given the similarity of snow and rain samples, our method also provides potential for the future analysis of amino acids in glacier ice cores. • A GC–MS analytical method for amino acids in precipitation was performed and optimized. • The developed method was characterized with high sensitivity and selectivity, while low blank value. • This is the first time that free amino acids were determined in the Tibetan Plateau rain. [ABSTRACT FROM AUTHOR]