A Process‐Based Model Integrating Remote Sensing Data for Evaluating Ecosystem Services

作者：Zhongen Niu, Honglin He, Shushi Peng, Xiaoli Ren, Li Zhang, Fengxue Gu, Gaofeng Zhu, Changhui Peng, Li Pan, Junbang Wang, Rong Ge, Na Zeng, Xiaobo Zhu, Yan Lv, Qingqing Chang, Qian Xu, Mengyu Zhang, Weihua Liu · 发表于：Journal of Advances in Modeling Earth Systems · 年份：2021 · DOI：10.1029/2020ms002451 · 被引用次数：62 · 研究领域：Plant Water Relations and Carbon Dynamics、Land Use and Ecosystem Services、Remote Sensing in Agriculture

Abstract Terrestrial ecosystems provide multiple services interacting in complex ways. However, most ecosystem services (ESs) models (e.g., InVEST and ARIES) ignored the relationships among ESs. Process‐based models can overcome this limitation, and the integration of ecological models with remote sensing data could greatly facilitate the investigation of the complex ecological processes. Therefore, based on the Carbon and Exchange between Vegetation, Soil, and Atmosphere (CEVSA) models, we developed a process‐based ES model (CEVSA‐ES) integrating remotely sensed leaf area index to evaluate four important ESs (i.e., productivity provision, carbon sequestration, water retention, and soil retention) at annual timescale in China. Compared to the traditional terrestrial biosphere models, the main innovation of CEVSA‐ES model was the consideration of soil erosion processes and its impact on carbon cycling. The new version also improved the carbon‐water cycle algorithms. Then, the Sobol and DEMC methods that integrated the CEVSA‐ES model with nine flux sites comprising 39 site‐years were used to identify and optimize parameters. Finally, the model using the optimized parameters was validated at 26 field sites comprising 135 site‐years. Simulation results showed good fits with ecosystem processes, explaining 95%, 92%, 76%, and 65% interannual variabilities of gross primary productivity, ecosystem respiration, net ecosystem productivity, and evapotranspiration, respectively. The CEVS...