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Water 2016, 8(12), 550; doi:10.3390/w8120550

Coupling Modified Linear Spectral Mixture Analysis and Soil Conservation Service Curve Number (SCS-CN) Models to Simulate Surface Runoff: Application to the Main Urban Area of Guangzhou, China

1,2,3
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1,4,5
,
1,2,3,* , 6
and
1,2,3
1
Guangzhou Institute of Geography, Guangzhou 510070, China
2
Key Laboratory of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangzhou 510070, China
3
Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangzhou 510070, China
4
Guangzhou Institute of Geochemistry, Guangzhou 510640, China
5
University of Chinese Academy of Sciences, Beijing 100049, China
6
Guangdong Meteorological Observation Data Center, Guangzhou 510080, China
*
Author to whom correspondence should be addressed.
Academic Editor: Ataur Rahman
Received: 12 October 2016 / Revised: 17 November 2016 / Accepted: 21 November 2016 / Published: 24 November 2016
View Full-Text   |   Download PDF [24694 KB, uploaded 24 November 2016]   |  

Abstract

Land surface characteristics, including soil type, terrain slope, and antecedent soil moisture, have significant impacts on surface runoff during heavy precipitation in highly urbanized areas. In this study, a Linear Spectral Mixture Analysis (LSMA) method is modified to extract high-precision impervious surface, vegetation, and soil fractions. In the modified LSMA method, the representative endmembers are first selected by combining a high-resolution image from Google Earth; the unmixing results of the LSMA are then post-processed to reduce errors of misclassification with Normalized Difference Built-up Index (NDBI) and Normalized Difference Vegetation Index (NDVI). The modified LSMA is applied to the Landsat 8 Operational Land Imager (OLI) image from 18 October 2015 of the main urban area of Guangzhou city. The experimental result indicates that the modified LSMA shows improved extraction performance compared with the conventional LSMA, as it can significantly reduce the bias and root-mean-square error (RMSE). The improved impervious surface, vegetation, and soil fractions are used to calculate the composite curve number (CN) for each pixel according to the Soil Conservation Service curve number (SCS-CN) model. The composite CN is then adjusted with regional data of the terrain slope and total 5-day antecedent precipitation. Finally, the surface runoff is simulated with the SCS-CN model by combining the adjusted CN and real precipitation data at 1 p.m., 4 May 2015. View Full-Text
Keywords: composite curve number; linear spectral mixture analysis; normalized difference built-up index; normalized difference vegetation index; runoff composite curve number; linear spectral mixture analysis; normalized difference built-up index; normalized difference vegetation index; runoff
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Xu, J.; Zhao, Y.; Zhong, K.; Ruan, H.; Liu, X. Coupling Modified Linear Spectral Mixture Analysis and Soil Conservation Service Curve Number (SCS-CN) Models to Simulate Surface Runoff: Application to the Main Urban Area of Guangzhou, China. Water 2016, 8, 550.

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