A study of the extraction of snow cover using nonlinear ENDSI model

doi:10.6046/gtzyyg.2018.01.09

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Abstract

Detecting snow cover information and snow space-time distribution quickly and accurately is a basic problem of ecological environment changes in the resources. Remote sensing technology effectively provides technical support for solving this problem. Normalized difference snow index (NDSI) is an important method for automatic extracting snow cover information using spectral features of snow, which have high reflection in the green band (0.53~0.59 μm) and strong absorption characteristics in short wave infrared band (1.57~1.65 μm). By using Landsat8 OLI images as the data source and according to the spectral characteristics of snow, the authors propose the enhanced normalized difference snow index (ENDSI) based on adding emissivity characteristics of snow in first band B1 (0.433~0.453 μm) and second band B2 (0.450~0.515 μm), and the utilization of this index to extract snow from OLI images. Simulation and case study results show the following characteristics: the sensitivity of ENDSI is stronger than that of NDSI for the snow thickness; with the increase of the thickness of snow, the change of ENDSI value is stronger than that of NDSI; ENDSI can effectively increase the difference between snow and non-snow; it is easy to extract snow from the image with 0.3 as ENDSI threshold and, in this way, snow extraction accuracy is improved.

Keywords ENDSI NDSI snow Landsat8 OLI

TP79

Issue Date: 08 February 2018

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	Haiyang PANG
	Xiangsheng KONG
	Lili WANG
	Yonggang QIAN

Cite this article:

Haiyang PANG,Xiangsheng KONG,Lili WANG, et al. A study of the extraction of snow cover using nonlinear ENDSI model[J]. Remote Sensing for Land & Resources, 2018, 30(1): 63-71.

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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2018.01.09 OR https://www.gtzyyg.com/EN/Y2018/V30/I1/63

Tab.1 Remote sensing data sources of research areas

Fig.1 Locations of research areas

Fig.2 Spectral curves of the main objects in the study area

Fig.3 Numbers and variation of extracted snow pixels by ENDSI and NDSI methods

Fig.4 Spectral curves of the snow and soil in study area 5

描述	公式	编号
积雪在短波红外波段反射率	ρ_snowswir= $∫ 1.57 1.65 ρ (λ) Γ (λ) dλ ∫ 1.57 1.65 Γ (λ) dλ$ =0.15	(7)
裸土在短波红外波段反射率	ρ_soilswir= $∫ 1.57 1.65 ρ (λ) Γ (λ) dλ ∫ 1.57 1.65 Γ (λ) dλ$ =0.25	(8)
积雪在可见光波段反射率	ρ_snow=ρ_{blue violet}+ρ_blue+ρ_green=3ρ_green-0.1	(9)
裸土在可见光波段反射率	ρ_soil=ρ_{blue violet}+ρ_blue+ρ_green=3ρ_green-0.06	(10)
积雪原始NDSI	NDSI_snow= $ρ green - 0.15 ρ green + 0.15$ (0.1≤ρ_green≤1)^①	(11)
裸土原始NDSI	NDSI_soil= $ρ green - 0.25 ρ green + 0.25$ (0≤ρ_green≤0.15)	(12)
积雪变换后的ENDSI	ENDSI_snow= $3 ρ green - 0.1 - 3.7 × 0.15 3 ρ green - 0.1 + 0.15$ (0.1≤ρ_green≤1)	(13)
裸土变换后的ENDSI	ENDSI_soil= $3 ρ green - 0.06 - 3.7 × 0.25 3 ρ green - 0.14 + 0.25$ (0≤ρ_green≤0.15)	(14)

Tab.2 ENDSI linear simulation formula

Fig.5 Variable trend graph of NDSI and ENDSI in green band (draw from a specific value)

Fig.6 Snow cover maps extracted by ENDSI and NDSI in study area 5

Tab.3 Statistics of extracted snow pixels by NDSI and ENDSI

Tab.4 Confusion matrix between validation area supervised classification and NDSI classification result

Tab.5 Confusion matrix between validation area supervised classification and ENDSI classification result

Fig.7 Accuracy verification example diagrams

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