• 遥感科学与应用技术 •

### 基于实测光谱模拟Landsat-8 OLI数据估算非光合 植被覆盖度

1. 鲁东大学资源与环境工程学院,烟台 264025
• 收稿日期:2018-04-19 修回日期:2018-08-25 出版日期:2018-11-20 发布日期:2018-11-20
• 通讯作者: 王静璞 E-mail:wang1636@sina.cn;wangjp@ldu.edu.cn
• 作者简介:

作者简介：王光镇(1991-),男,硕士生,主要从事草原植被遥感监测研究。E-mail: wang1636@sina.cn

• 基金资助:
国家自然科学基金青年科学基金项目（41701005）;国家自然科学基金重点项目(41330746);山东省自然科学基金培养项目（ZR2017PD006）

### Estimating Fractional Cover of Non-photosynthetic Vegetation Using Field Spectral to Simulate Landsat-8 OLI

WANG Guangzhen(), WANG Jingpu*(), HAN Liu, CHAI Guoqi, WANG Zhoulong

1. College of Resource and Environment Engineering, Ludong University, Yantai 264025, China
• Received:2018-04-19 Revised:2018-08-25 Online:2018-11-20 Published:2018-11-20
• Contact: WANG Jingpu E-mail:wang1636@sina.cn;wangjp@ldu.edu.cn
• Supported by:
National Natural Science Foundation of China, No.41701005;Key Program of National Natural Science Foundation of China, No.41330746;Shandong Provincial Natural Science Foundation, China, No.ZR2017PD006.

Abstract:

Quantitative estimation fractional cover of non-photosynthetic vegetation (fNPV) is critical for grassland ecosystem carbon storage, vegetation productivity, soil wind erosion. Remote sensing is an important tool for estimating the fractional cover of non-photosynthetic vegetation as a key descriptor of grassland ecosystem function. Developing tools that allow for monitoring of non-photosynthetic vegetation in space and time is a key step needed to improve management of grassland. In this paper, the data source of the ground-based measured endmember spactrum, mixed scenario spactrum and coverage information is presented. Firstly, we used the mean spectrum of NPV, PV and BS three components to simulate the mixed spectral through a linear spectral mixture model, and then the correlation between different multispectral indices and fNPV was evaluated. On this basis, explore the NDVI-DFI feature space correspond to the fundamental assumption of ternary linear mixed model. Finally, the validity of the estimate fNPV of multispectral index is verified by the field mixed scenario. The results show that the SWIR band is the sensitivity of the NPV, PV and BS, based on the improved OLI-DFI index are effective to distinguish the NPV, PV and BS potential. In the simulate mixed scenario, OLI-DFI and MODIS-DFI index correlated with fNPV, the coefficient of determination (R2) was 0.84 and 0.94, and root mean square error(RMSE) was 0.09 and 0.05(n=66, p<0.001). However, the NDI and NDSVI index correlation with fNPV is very low. Additionally, compared to simulate the mixing, in the field mixed scenario, OLI-DFI and MODIS- DFI index for estimating the effectiveness of the fNPV all have a certain degree of decline, R2 was 0.65 and 0.75, RMSE was 0.14 and 0.12, respectively. Based on the OLI data to construct the NDVI-DFI feature space to satisfy the fundamental assumption of ternary linear mixed model, which can effectively estimate the fNPV. The research will provide theoretical basis for the estimation of fNPV multispectral remote sensing.