The Spatial–Spectral–Environmental Extraction Endmember Algorithm and Application in the MODIS Fractional Snow Cover Retrieval

Hongyu Zhao,Hongyi Li,Bo Su,Guanghui Huang,Xiaohua Hao,Huajin Lei,Jian Wang,Donghang Shao,Xiaojing Hu

doi:10.3390/rs12223693

Abstract

Endmember extraction is a primary and indispensable component of the spectral mixing analysis model applicated to quantitatively retrieve fractional snow cover (FSC) from satellite observation. In this study, a new endmember extraction algorithm, the spatial–spectral–environmental (SSE) endmember extraction algorithm, is developed, in which spatial, spectral and environmental information are integrated together to automatically extract different types of endmembers from moderate resolution imaging spectroradiometer (MODIS) images. Then, combining the linear spectral mixture analysis model (LSMA), the SSE endmember extraction algorithm is practically applied to retrieve FSC from standard MODIS surface reflectance products in China. The new algorithm of MODIS FSC retrieval is named as SSEmod. The accuracy of SSEmod is quantitatively validated with 16 higher spatial-resolution FSC maps derived from Landsat 8 binary snow cover maps. Averaged over all regions, the average root-mean-square-error (RMSE) and mean absolute error (MAE) are 0.136 and 0.092, respectively. Simultaneously, we also compared the SSEmod with MODImLAB, MODSCAG and MOD10A1. In all regions, the average RMSE of SSEmod is improved by 2.3%, 2.6% and 5.3% compared to MODImLAB for 0.157, MODSCAG for 0.157 and MOD10A1 for 0.189. Therefore, our SSE endmember extraction algorithm is reliable for the MODIS FSC retrieval and may be also promising to apply other similar satellites in view of its accuracy and efficiency.

Highlights

Snow cover plays a crucial role in regulating energy budgets, hydrologic cycles, and climate change
The results of all evaluated metrics show that our method can significantly improve fractional snow cover (FSC) retrieval accuracy in different snow cover areas compared to other spectral mixture analysis models
Compared to the RMSE of MODImLAB and MODIS snow covered area and grain sizes (MODSCAG), the accuracy of SSEmod is improved by 3.1% and 3.6% in forest areas, respectively, and in non-forest areas, it improved by 1.9% and 2.2%

Summary

Introduction

Snow cover plays a crucial role in regulating energy budgets, hydrologic cycles, and climate change. The snowmelt runoff offers an essential supply for fresh-water resources at mid-latitude regions [1,2,3]. Snow cover is a vital input parameter for hydrologic and climate models [4,5,6]. The monitoring and research of snow cover over long time-series are pivotal to provide a scientific understanding of its role in the earth system and human society. The high–accuracy snow cover data can effectively improve the simulation accuracy of climate and hydrological models [7,8,9]. It is of great significance to develop a more accurate algorithm for snow cover evaluation

Methods

Findings

Discussion

Conclusion