从遥感观测数据到数据产品

doi:10.11821/dlxb201711013

摘要/Abstract

摘要：

本文将遥感作为一种观测手段,通过梳理遥感从观测数据到数据产品的处理方法,分析了目标识别和参数提取所采用的方法、特点与存在的问题,发现遥感从观测数据到数据产品的过程至今仍未形成系统、科学的方法论,指出遥感方法论的建立需通过挖掘多源、多角度、多时相、多光谱、主被动协同的遥感观测数据隐含的深层指示性特征,加强结构化方法研究,构建新型的、可重复、易于处理且能够反映物理、化学、地学、生态学、生物学意义的遥感指标,以数据产品为导向发展多源协同遥感观测与分析处理方法,推动遥感从观测数据到数据产品的处理方法向标准化、结构化转变。

关键词: 遥感观测, 数据产品, 目标识别, 参数提取, 经验/半经验模型, 物理模型

Abstract:

This article takes remote sensing as one of measurements. The paper overviews the general methodology from remote sensing observations to data products, and categorizes the existing methods into two types: target recognition and parameter retrieval together with their features, advantages and shortcomings. Even after 50 years of continuous research, we are still lack of consistent and scientific methodology to produce data products from remote sensing observations. In the future, in order to build up scientific and structured remote sensing methods for data products, the priority should be given to further development of multi-angle, multi-temporal, multi-spectral, and multi-source as well as both active and passive remote sensing observations, so as to develop new remote sensing indices, which have obvious ecological, geographic, agronomic meanings, to promote the normalization and standardization of remote sensing methods, and to generate synthetic products based on all available remote sensing observations instead of single remote sensing observations. Big data and cloud computing will provide support for the process from remote sensing observations to data products.

Key words: remote sensing observations, remote sensing data products, target recognition, parameter retrieval, empirical/semi-empirical algorithm, physical model

吴炳方, 张淼. 从遥感观测数据到数据产品[J]. 地理学报, 2017, 72(11): 2093-2111.

Bingfang WU, Miao ZHANG. Remote sensing: Observations to data products[J]. Acta Geographica Sinica, 2017, 72(11): 2093-2111.

图/表 2

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共性方法		MODIS数据产品^*	欧空局哥白尼计划陆地数据产品	GLASS数据产品
目标识别		云掩膜;雪覆盖;土地覆盖;热异常—火点;海冰覆盖;土地覆盖动态变化;火烧迹地	水体¹;火烧迹地¹;
参数提取	遥感指数	植被指数(NDVI/EVI)	NDVI¹;植被状况指数(VCI)¹
	经验/半经验模型	总初级生产力(GPP)/净初级生产力(NPP);	VPI¹;DMP(干物质生产力)¹;地表温度(LST)ⁿ;Albedo¹;	Albedoⁿ;裸土宽波段发射率ⁿ;长波净辐射ⁿ;净辐射ⁿ;潜热通量ⁿ;GPPⁿ
	物理模型	气溶胶;可降水量;云产品(云相、云光学厚度、有效云粒径、云顶温度、高度等);地表温度和比辐射率;大气剖面;LAI/FAPAR;蒸散发;Albedo/BRDF	LAI¹;FAPAR¹;fCover¹;土壤水分¹;反射率¹	LAIⁿ;FAPARⁿ;fCoverⁿ;植被区宽波段发射率ⁿ;下行短波辐射ⁿ;光合有效辐射ⁿ

数据产品	植被类型	相对误差	均方根误差	文献
MODIS Terra LAI	混合类型1^*	-	1.07~2.08 (与有效LAI对比) 1.42 (与真实LAI对比)	[158-160]
MODIS Aqua LAI	混合类型1^*	-	1.74 (与有效LAI对比) 1.53 (与真实LAI对比)	[158]
MODIS Terra & Aqua LAI	农田	88%	0.5~1.05	[161]
	森林	35%~65%	-	[161]
	草地	47%	-	[161]
	混合类型2#	-	1.29 (与有效LAI对比) 1.14 (与真实LAI对比)	[154]
	混合类型1^*	-	1.63 (与有效LAI对比) 1.09 (与真实LAI对比)	[158]
VGT LAI	农田	44%	0.5~1.05	[161]
	森林	25%~37%	-	[161]
	草地	76%	-	[161]
CYCLOPES LAI	混合类型2^*	-	0.73 (与有效LAI对比) 0.84 (与真实LAI对比)	[143]
CYCLOPES LAI	混合类型1^*	-	0.50~1.34 (与有效LAI对比) 0.97 (与真实LAI对比)	[158-159]
GLASS LAI	混合类型1^*	-	0.78~0.87	[159-160]