ESTARFM算法在长江中下游平原地区的适用性研究

doi:10.12082/dqxxkx.2021.200404

摘要/Abstract

摘要：

时空融合技术是目前解决单一遥感数据源难以同步获取高时空分辨率数据的有效途径。然而,如何设置参数使模型融合效果最佳,如何设置在植被监测中广泛应用的植被指数的融合步骤,进而获得最佳的植被指数时序数据,目前仍不明晰。本文以长江中下游平原地区的典型县域—南昌县为例,基于Landsat和MODIS多时相数据对当前主流时空融合模型—ESTARFM （Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model）进行参数敏感性分析,并系统地对比分析了2组融合实验RI（先融合波段反射率后计算植被指数）和IR（先计算植被指数后直接融合）的融合效果。结果表明： ① ESTARFM算法中参数的敏感性在波段反射率、植被指数融合中表现出相似的特征,随着滑动窗口与相似像元数量的增大,融合误差整体呈现出先减小后趋于稳定或增大的趋势;在ESTARFM算法应用中,存在着最佳参数设置范围;② 相较于RI组,IR组模拟结果精度更高（R²_RI-NDVI=0.866,R²_IR-NDVI=0.953,R²_RI-EVI =0.814,R²_IR-EVI=0.930）,且能够较好地削弱“斑块”现象,更好地表征出细小地物和纹理特征。研究结果为遥感数据时空融合模型在地块破碎、种植制度多变的复杂环境中的应用提供借鉴和参考。

关键词: 时空融合技术, ESTARFM, Landsat, MODIS, 波段反射率, NDVI, EVI, 长江中下游平原

Abstract:

The spatial-temporal fusion technology is an effective tool to blend observations from sensors with different spatial and temporal characteristics. The ESTARFM algorithm has good applicability to areas with fragmented land, and is susceptible to meteorological conditions, and has important practical significance for resource and environmental monitoring in southern China. However, how to select the fusion scheme and set the parameters of the fusion model to achieve the best fused time series vegetation index data is still unclear. This paper takes Nanchang County, a typical county in the middle and lower reaches of the Yangtze River, as an example, to analyze the parameters sensitivity of the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) for fusing multi-temporal Landsat and MODIS data. Based on Normalized Differential Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI), we systematically compared the performance of vegetation index fusion using the RI scheme (band reflectance was fused firstly and then the vegetation index was calculated) and IR scheme (vegetation index was calculated firstly and then directly fused). The results show that: (1) The sensitivity of parameters (sliding window size and number of similar pixels) in ESTARFM displayed similar characteristics in both the fusion of band reflectance and vegetation index. With the increase of sliding window size and number of similar pixels, the R ² and SSIM values of band reflectance and vegetation index increased firstly and then remained steady or decreased, while the overall fusion error (MAE and RMSE values) decreased firstly and then remained steady or increased. There was an optimal parameter setting range in the application of ESTARFM model. The parameter sensitivity analysis is required to determine the optimal parameter range when adopting ESTARFM algorithm in different regions; (2) Compared with the RI scheme, the IR scheme had a higher fusion accuracy (R²_RI-NDVI=0.866, R²_IR-NDVI=0.953, R²_RI-EVI =0.814, R²_IR-EVI =0.930). It produced less outliers and noise during the fusion process and can effectively weaken the "pattern spot" and preserve spatial details and texture features, resulting in a high similarity with the real image. In addition, based on Landsat and MODIS multi-temporal images, the ESTARFM algorithm can also be used to generate high-temporal-resolution images to approximately replace the cloud and cloud shadow areas in Landsat images, which can effectively overcome the "cloud pollution" phenomenon in cloudy and rainy areas and improve remote sensing data quality. Our results provide a reference for the application of spatial-temporal fusion model in the complex environment with fragmented land and changeable planting systems.

Key words: spatial-temporal fusion technology, ESTARFM, Landsat, MODIS, band reflectance, NDVI, EVI, in the middle-lower Yangtze Plain

管琪卉, 丁明军, 张华, 王鹏. ESTARFM算法在长江中下游平原地区的适用性研究[J]. 地球信息科学学报, 2021, 23(6): 1118-1130.DOI:10.12082/dqxxkx.2021.200404

GUAN Qihui, DING Mingjun, ZHANG Hua, WANG Peng. Analysis of Applicability about ESTARFM in the Middle-Lower Yangtze Plain[J]. Journal of Geo-information Science, 2021, 23(6): 1118-1130.DOI:10.12082/dqxxkx.2021.200404

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波段		传感器谱段/μm		空间分辨率/m
OLI波段	MODIS波段	OLI	MODIS	OLI	MODIS
band2(蓝)	band3（蓝）	0.45~0.51	0.459~0.479	30	500
band4（红）	bnad1（红）	0.64~0.67	0.620~0.670	30	250
band5（近红外）	band2（近红外）	0.85~0.88	0.841~0.876	30	250

Landsat 8 OLI			MODIS
影像时间	数据用途	云量/%	影像时间	数据用途
2015-08-24	融合输入	17.27	2015-08-21	融合输入
2015-09-09	融合验证	1.45	2015-09-06	融合输入
2015-10-11	融合输入	0.62	2015-10-08	融合输入

	R²/×10²		SSIM/×10²		MAE/×10⁴		RMSE/×10⁴		time/h
	RI	IR	RI	IR	RI	IR	RI	IR	RI	IR
NDVI	86.57	95.34	99.89	99.9	709.9	442.8	1032.2	655.3	18	9
EVI	81.43	93.04	99.72	99.96	778.7	449.8	1069.0	637.8	21	9