在全球气候变化与节能减排背景下，准确评估陆地生态系统碳收支空间格局及其影响机制具有重要意义。作为生态脆弱但蕴含较大固碳潜力的中国西南喀斯特岩溶生态地区，受到复杂地理环境、观测条件的限制，其生态系统碳源/汇强度、空间格局及调控机制尚处于“灰箱”的研究状态。本项目拟以陆地生态系统总初级生产力GPP为研究对象，利用西南地区逐步建立起来的通量观测网，结合卫星遥感数据，反演得到光能利用率GPP估算模型参量，采用基于贝叶斯原理的马尔科夫链－蒙特卡罗方法，优化各植被类型最大光能利用率εmax，降低山区复杂地表碳循环参数反演的不确定性，得到高时空精度的GPP估算结果。对GPP与气候因子、环境因子、植被状态因子进行敏感性分析，辨析自然和人为因素对陆地生态系统碳循环过程的影响机制，提升对西南喀斯特岩溶地区碳循环过程机理的理解，探讨西南地区碳循环过程的时空演变趋势，更好地服务于区域可持续发展。

In the context of negotiations on global climate change and energy conservation, accurate monitoring and quantitative evaluation of the carbon budget of terrestrial ecosystems is a hot topic worldwide. The study of the terrestrial ecosystem carbon cycle, especially in the karst southwest China, is still in a puzzling situation limited by poor observation and monitoring uncertainty. Both observation and modelling researches are urgently needed to be further developed. This project aims to take gross primary productivity (GPP) which is the key parameter in carbon cycle to evaluate the carbon dynamics in Southwest China. Comprehensively using multi-source data such as meteorological data, optical remote sensing data, and reanalysis data to drive the GPP model based on light use efficiency (LUE) method. Related to the maximum light use efficiency εmax which is the most important variable in the model, Markov chain Monte Carlo (MCMC) methods based on Bayes principle is adopted to derive the best εmax for each biome type. It is expected to better reveal the GPP in the spatial evolution and temporal trend over the mountainous areas of Southwest China, and to improve the understanding of energy cycle in those areas. The version of this research is to improve our understanding of the land surface process mechanism in the southwestern region, and better serve the regional ecological environment recovery and protection act, and play a positive role in supporting regional sustainable development and global change.