Greenhouse gas (GHG) emissions from rivers and lakes have been shown to significantly contribute to global carbon and nitrogen cycling. In spatiotemporal-variable and human-impacted rivers in the grassland region, simultaneous carbon dioxide, methane and nitrous oxide emissions and their relationships under the different land use types are poorly documented. This research estimated greenhouse gas (CO₂, CH₄, N₂O) emissions in the Xilin River of Inner Mongolia of China using direct measurements from 18 field campaigns under seven land use type (such as swamp, sand land, grassland, pond, reservoir, lake, waste water) conducted in 2018. The results showed that CO₂ emissions were higher in June and August, mainly affected by pH and DO. Emissions of CH₄ and N₂O were higher in October, which were influenced by TN and TP. According to global warming potential, CO₂ emissions accounted for 63.35% of the three GHG emissions, and CH₄ and N₂O emissions accounted for 35.98% and 0.66% in the Xilin river, respectively. Under the influence of different degrees of human-impact, the amount of CO₂ emissions in the sand land type was very high, however, CH₄ emissions and N₂O emissions were very high in the artificial pond and the wastewater, respectively. For natural river, the greenhouse gas emissions from the reservoir and sand land were both low. The Xilin river was observed to be a source of carbon dioxide and methane, and the lake was a sink for nitrous oxide.

It is difficult to accurately identify and extract bodies of water and underwater vegetation from satellite images using conventional vegetation indices, as the strong absorption of water weakens the spectral feature of high near-infrared (NIR) reflected by underwater vegetation in shallow lakes. This study used the shallow Lake Ulansuhai in the semi-arid region of China as a research site, and proposes a new concave–convex decision function to detect submerged aquatic vegetation (SAV) and identify bodies of water using Gao Fen 1 (GF-1) multi-spectral satellite images with a resolution of 16 meters acquired in July and August 2015. At the same time, emergent vegetation, “Huangtai algae bloom”, and SAV were classified simultaneously by a decision tree method. Through investigation and verification by field samples, classification accuracy in July and August was 92.17% and 91.79%, respectively, demonstrating that GF-1 data with four-day short revisit period and high spatial resolution can meet the standards of accuracy required by aquatic vegetation extraction. The results indicated that the concave–convex decision function is superior to traditional classification methods in distinguishing water and SAV, thus significantly improving SAV classification accuracy. The concave–convex decision function can be applied to waters with SAV coverage greater than 40% above 0.3 m and SAV coverage 40% above 0.1 m under 1.5 m transparency, which can provide new methods for the accurate extraction of SAV in other regions.