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As water‐related ecosystem services (WRES) encounter unprecedented threats with climate change and human activities, it is important to understand the impact of variations in the supply and demand for WRES on Sustainable Development Goals (SDGs). Previous studies commonly overlooked water quality and water‐use sectors when using comprehensive supply‐demand index (SDIcom), defined as the difference between water supply and demand, leading to challenges in applying WRES estimation to SDGs assessment. We constructed a refined supply‐demand framework for WRES, categorizing the water supply by water quality and the water demand by sectors. It allows the calculation of refined supply‐demand index (SDIref), addressing the limitations of SDIcom. Our findings demonstrate that SDIref could be effectively employed to assess SDGs and rectify the overestimation of SDIcom in the case study in Xinjiang. The supply‐demand relationship of water resources indicates surpluses in domestic and industrial water use and deficits in agricultural and ecological water use, with the latter worsening. SDIref is projected to be higher in the scenario with SSP2‐4.5 and high growth rate of irrigation efficiency. Our study suggested informed decisions based on SDG‐specific targets to either maintain or enhance SDG progress scores. The proposed refined supply‐demand framework of WRES established a quantitative link between WRES and human well‐being, providing applicable insights for other ecosystem service assessments in other regions. Plain Language Summary: The study on the supply and demand of water‐related ecosystem services (WRES) describes the process of WRES transmission from water‐related ecosystems to human society. The variations in these transmissions arising from internal differences in water quality and water‐use sectors are pivotal factors in the effects of WRES to Sustainable Development Goals (SDGs). Unfortunately, these critical factors were generally neglected. Based on above‐mentioned gap, we proposed a refined supply‐demand framework of WRES, introducing the refined supply‐demand index (SDIref). SDIref, which accounts for water quality in supply and water‐use sectors in demand, exhibits superior performance in SDGs assessment. In the case study of Xinjiang, SDIref could more accurately capture the supply‐demand relationship of water resources compared to the comprehensive supply‐demand index (SDIcom) that is the difference between water supply without considering quality and demand without sectors. Domestic and industrial water resources are projected to be in surplus and agricultural and ecological water resources will face worsening deficit. The most sustainable water supply‐demand relationship is anticipated in the scenario with moderate greenhouse gas emissions and a high growth rate of irrigation efficiency. In light of the specific SDG assessment, we posed appropriate recommendations aimed at improving SDG progress scores. This study could offer valuable insights for integrating WRES into decision‐making towards enhancing human well‐being. Key Points: This study proposed a refined supply‐demand framework for ecosystem services to assess the Sustainable Development GoalsThe refined supply‐demand index could provide more accurate assessments on ecosystem services compared to the comprehensive supply‐demand indexThe relationship between water supply and demand in Xinjiang will undergo a worsening deficit in the future [ABSTRACT FROM AUTHOR]

China faces increasingly serious water scarcity due to the uneven distribution of available water resources, rapid economic development, and water pollution. The current war on water pollution by the Chinese government requires nationwide water quality information at high spatiotemporal resolution that can be obtained by only remote sensing (RS) methods. However, it is challenging to remotely retrieve such information from turbid Case-2 waters. This paper reviews four aspects of the major achievements in remotely sensed coastal and inland water quality in China. Specifically, achievements in atmospheric correction prior to water quality retrieval, progress in water-related sensor design, developments (improvements) to existing Case-2 water algorithms, and advances in oil spill and harmful algal bloom monitoring. Major challenges are identified, including: 1) a large mismatch exists between the water quality information required and RS datasets due to a lack of professional inland water sensors; 2) planned monitoring and field experiments for studying the optical properties of inland waters are scarce; and 3) RS of urban black odorous waters and international rivers is of great urgency. This review may provide scientific guidelines for obtaining information about coastal and inland waters and assist water resource managers and aquatic ecologists in controlling water pollution. [ABSTRACT FROM AUTHOR]

Detecting and segmenting individual trees in forest ecosystems with high-density and overlapping crowns often results in bias due to the limitations of the commonly used canopy height model (CHM). To address such limitations, this paper proposes a new method to segment individual trees and extract tree structural parameters. The method involves the following key steps: (1) unmanned aerial vehicle (UAV)-scanned, high-density laser point clouds were classified, and a vegetation point cloud density model (VPCDM) was established by analyzing the spatial density distribution of the classified vegetation point cloud in the plane projection; and (2) a local maximum algorithm with an optimal window size was used to detect tree seed points and to extract tree heights, and an improved watershed algorithm was used to extract the tree crowns. The proposed method was tested at three sites with different canopy coverage rates in a pine-dominated forest in northern China. The results showed that (1) the kappa coefficient between the proposed VPCDM and the commonly used CHM was 0.79, indicating that performance of the VPCDM is comparable to that of the CHM; (2) the local maximum algorithm with the optimal window size could be used to segment individual trees and obtain optimal single-tree segmentation accuracy and detection rate results; and (3) compared with the original watershed algorithm, the improved watershed algorithm significantly increased the accuracy of canopy area extraction. In conclusion, the proposed VPCDM may provide an innovative data segmentation model for light detection and ranging (LiDAR)-based high-density point clouds and enhance the accuracy of parameter extraction. [ABSTRACT FROM AUTHOR]