Microwave remote sensing can provide long-term near-surface soil moisture data on regional and global scales. Conducting standardized authenticity tests is critical to the effective use of observed data products in models, data assimilation, and various terminal scenarios. Global Land Data Assimilation System (GLDAS) soil moisture data were used as a reference for comparative analysis, and triple collocation analysis was used to validate data from four mainstream passive microwave remote sensing soil moisture products: Soil Moisture and Ocean Salinity (SMOS), Soil Moisture Active and Passive (SMAP), Global Change Observation Mission–Water using the Advanced Microwave Scanning Radiometer 2 (AMSR2) instrument, and Fengyun-3C (FY-3C). The effects of topography, land cover, and meteorological factors on the accuracy of soil moisture observation data were determined. The results show that SMAP had the best overall performance and AMSR2 the worst. Passive microwave detection technology can accurately capture soil moisture data in areas at high altitude with uniform terrain, particularly if the underlying surface is soil, and in areas with low average temperatures and little precipitation, such as the Qinghai–Tibet Plateau. FY-3C performed in the middle of the group and was relatively optimal in northeast China but showed poor data integrity. Variation in accuracy between products, together with other factors identified in the study, provides a baseline reference for the improvement of the retrieval algorithm, and the research results provide a quantitative basis for developing better use of passive microwave soil moisture products.

Understanding how nitrogen (N) and/or phosphorus (P) addition affects plants carbon- and water- related ecophysiological characteristics is essential for predicting the global change impact on the alpine meadow ecosystem structure and function in carbon and water cycling. The Qinghai-Tibetan Plateau (QTP) with the largest alpine meadow in the world is regarded as the third pole in the earth and has been experiencing increased atmospheric N deposition. In this project, we focused on two key species (Elymus dahuricus and Gentiana straminea) of the alpine meadow on the Tibetan Plateau and investigated the variability of photosynthetic and stomatal responses to 8-year N and/or P treatments through field measurements and modeling. We measured photosynthesis- and g_s-response curves to generate parameter estimates from individual leaves with two widely used stomatal models (the BWB model and MED model) for validation of growth and ecosystem models and to elucidate the physiological basis for observed differences in productivity and WUE. We assessed WUE by means of gas exchange measurements (WUE_i) and stable carbon isotope composition (Δ¹³C) to get the intrinsic and integrated estimates of WUE of the two species. P and N+P treatments, but not N, improved the photosynthetic capacity (A_net and V_cmax) for both species. Stomatal functions including instaneous measurements of stomatal conductance, intrinsic water-use efficiency and stomatal slope parameters of the two widely used stomatal models were altered by the addition of P or N+P treatment, but the impact varied across years and species. The inconsistent responses across species suggest that an understanding of photosynthetic, stomatal functions and water-use should be evaluated on species separately. WUE estimated by Δ¹³C values had a positive relationship with A_net and g_s and a negative relationship with WUE_i. Our findings should be useful for understanding the underlying mechanisms of the response of alpine plants growth and alpine meadow ecosystem to global change.

The lack of reliable near-surface wind data products in lake district regions significantly limits the efficiency of meteorological models, forecasts, services, and wind resource development. This is primarily due to a shortage of observation data and related studies, as well as insufficient validation and promotion of existing data products, which further hinders the comprehension and monitoring of local wind fields. In this study, comprehensive verification research was conducted on mainstream wind field products, namely, ERA5-Land (EC), GLDAS (GL), and HRCLDAS (HR) in the Dongting Lake area of China. This was achieved by utilizing a large volume of measured data and a triple collocation analysis (TCA) method. Additionally, an exploration into the optimal wind field data fusion method was undertaken. HR products demonstrate superior performance in capturing wind speed at the in situ measured scale, while GL outperforms at the grid scale, and EC products show relatively stable performance with minimal outliers. The long short-term memory (LSTM) neural network model, combined with time-series features, emerges as the most optimal data fusion model. LSTM fusion product is superior to the original product (except for HR products at the in situ measured scale), TCA-based weighted fusion products, and multi-layer fully connected neural network (MFCNN) on various parameters. This study quantifies the performance of mainstream wind products in lake areas and provides a benchmark for further application of these products. Furthermore, the successful implementation of an optimal wind data fusion method can provide valuable insights for related research, and the resulting wind fusion products can offer superior basic data support for local terminal applications.