Snow cover is an important water resource in arid and semi-arid regions of Central Asia, and is related to agricultural and livestock production, ecosystems, and socio-economic development. The snowline altitude (SLA) is a significant indicator for monitoring the changes in snow cover in mountainous regions under the changing climate. Here, we investigate the spatiotemporal variation of SLA in the Tienshan Mountains (TS) during 2001–2019 using Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover products on a grid-by-grid basis. The potential influence of topographic factors (slope gradient and aspect) on SLA and the correlation between SLA, temperature, precipitation, and solar radiation are also investigated. The results are as follows: (1) The annual cycle of SLA shows strong seasonal fluctuations (from about 2000 m in late December to 4100 m in early August). The SLA over the TS exhibits a large spatiotemporal heterogeneity. (2) SLA increases with a steeper slope gradient. The SLA of the northerly aspect is generally less than the southerly. (3) The SLA over the TS generally shows an increasing trend in the recent years (2001–2019). The change trend of SLA varies in different months. Except for a slight decrease in June, the SLA increased in almost all months, especially at the start of the melt season (March and April) and the end of melting season (July and August). (4) The SLA increases with increased temperature/radiation in the TS, and decreases with increased precipitation. Solar radiation is the dominant climatic factor affecting the changes of SLA in the TS. Compared with precipitation, temperature is more correlated to SLA dynamics.

Accurate and timely acquisition of cropping intensity and spatial distribution of paddy rice is not only an important basis for monitoring growth and predicting yields, but also for ensuring food security and optimizing the agricultural production management system of cropland. However, due to the monsoon climate in southern China, it is cloudy and rainy throughout the year, which makes it difficult to obtain accurate information on rice cultivation based on optical time series images. Conventional image synthesis is prone to omission or redundancy of spectral and temporal features that are potentially important for rice-growth identification, making it difficult to determine the optimal features for high-precision mapping of paddy rice. To address these issues, we develop a method to granulate the effective use interval of classification features by extracting phenological signatures of rice to obtain cost-effective and highly accurate mapping results. Two steps are involved in this method: (1) analyzing the information on various features (spectra, polarization, and seasonal regularity) to identify three key phenological periods throughout the lifespan of paddy rice; (2) identifying the features with the highest class separation between paddy rice, non-paddy crops, and wetlands under different phenological stages; and (3) removing redundant features to retain the optimal feature combinations. Subsequently, the obtained feature sets are used as input data for the random forest classifier. The results showed that the overall accuracy of the identified rice results was 95.44% with F1 scores above 93% for both single- and double-cropping rice. Meanwhile, the correlation coefficient of our mapped rice area compared with the official statistics of rice area at county and district levels was 0.86. In addition, we found that combining Sentinel-1 and Sentinel-2 images for rice recognition was better than using Sentinel-1 or Sentinel-2 alone, and the classification accuracy was improved by 5.82% and 2.39%, which confirms that the synergistic Sentinel-1 and Sentinel-2 data can effectively overcome the problem of missing optical images caused by clouds and rain. Our study demonstrates the potential of distinguishing mixed rice-cropping systems in subtropical regions with fragmented rice-field distribution in a cloudy and rainy environment, and also provides a basis for the rational layout of rice production and improvement of cultivation systems.

Estimating organic carbon fluxes in data-limited tropical island catchments is constrained by sparse discharge records and concentration measurements. For the Changhua River, Hainan Island, we compared three calendar-month runoff-coefficient models based on the mean, median, and geomet-ric mean with a linear model incorporating current- and previous-month precipitation. Models were fitted to monthly records from 2011–2023 and evaluated against all 12 months of 2024. Holdout root mean square errors were 42.669, 39.544, 38.382, and 31.801 mm, respectively. The dual-precipitation model performed best, with a Nash–Sutcliffe efficiency of 0.763 and a percentage bias of −10.220%. Its fixed parameters were applied to September–November 2025 precipitation. The resulting monthly equivalent runoff coefficients were transferred to Ningyuan and Lingshui using their precipitation and drainage areas. Estimated runoff volumes were multiplied by equally weighted mean concentrations from three October–November surveys. Conditional dissolved or-ganic carbon exports were 2763.373, 730.024, and 655.773 t C for Changhua, Ningyuan, and Lingshui, respectively; corresponding particulate organic carbon exports were 571.135, 201.645, and 441.045 t C. These estimates depend on unverified catchment transferability and concentration rep-resentativeness, particularly for unsampled September. They apply to a limited observation period and do not establish annual carbon budgets or carbon sequestration.