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摘要： 随着我国遥感技术迅速的发展，国产系列卫星数据越来越多的应用到各个行业中。在湿地遥感监测方面，湿地生物量和碳储量的遥感估算研究是研究人员非常关注的研究问题，我国自主研制的高分（GF）系列卫星为湿地生态系统的资源监测提供新的途径和方法。提出了基于GF-1卫星的若尔盖高寒沼泽湿地地上生物量与土壤有机碳密度估算方法，通过选取GF-1遥感数据单波段信息，计算植被指数信息、纹理特征、地形特征等27个遥感因子，采用逐步回归法确定建模因子，构建了若尔盖湿地地上生物量和有机碳密度估算模型。研究结果表明：整个若尔盖湿地地上生物量为109.93万t，0~30 cm的土壤有机碳密度为18.99 kg/m2。经地面调查数据验证，地上生物量估算精度为86.44%，有机碳密度估算精度为81.56%；并且，地上生物量和土壤有机碳密度与研究区的湿地植被分布主要集中在中部和西北部范围的空间特征一致，模型估算出的研究结果具有较好的可靠性和合理性。 关键词： 高分卫星;  高寒沼泽湿地;  地上生物量;  土壤有机碳;  遥感估算     Abstract: With the rapid development of remote sensing technology in China， more and more domestic satellite data are applied to various industries. In the field of wetland remote sensing monitoring， remote sensing estimation of wetland biomass and carbon storage is a research area of great concern to researchers In particular， the use of our own developed GF series of satellites for wetland ecosystem resources monitoring to provide a new way and method. In this paper， GF-1 satellite-based method was developed to estimate the aboveground biomass and soil organic carbon density of Alpine marsh wetlands in Ruo'ergai By selecting and calculating 27 remote sensing factors such as single band information， vegetation index information， texture feature and terrain feature of GF-1 remote sensing data， the modeling factors are determined by stepwise regression method an estimation model of aboveground biomass and organic carbon density in zoige wetland was constructed. The results showed that the aboveground biomass of the whole zoige wetland was 1.09 million tons， the soil organic carbon density of 0~30 cm soil was 18.99 kg/m2.Through field investigation， the estimation accuracy of aboveground biomass was 86.44%， and the estimation accuracy of organic carbon density was 81.56%. Moreover， the aboveground biomass and soil organic carbon density are consistent with the spatial characteristics of the wetland vegetation distribution in the study area， which is mainly concentrated in the middle and northwest. The research results estimated by the model are of good reliability and rationality.

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