A Combined Vegetation Cover and Temperature-Emissivity Separation (V-TES) Method to Estimate Land Surface Emissivity

发射率 遥感 环境科学 植被(病理学) 土地覆盖 表面粗糙度 航程(航空) 气象学 材料科学 地质学 光学 物理 土地利用 工程类 病理 土木工程 复合材料 医学
作者
Sofia L. Ermida,Glynn Hulley,Frank-M. Göttsche,Isabel F. Trigo
出处
期刊:IEEE Transactions on Geoscience and Remote Sensing [Institute of Electrical and Electronics Engineers]
卷期号:61: 1-18 被引量:4
标识
DOI:10.1109/tgrs.2023.3301615
摘要

Land Surface Emissivity (LSE) is a critical variable in the quantification of the surface energy budget and for the estimation of surface parameters from earth observation data, in particular the Land Surface Temperature (LST). A new LSE product is proposed that combines two widely used methods: the Vegetation Cover Method (VCM) and the Temperature Emissivity Separation (TES) algorithm. The so-called V-TES approach maximizes the strengths of each method, considering their different performance over a wide range of surface conditions. As such, over vegetated areas, where thermal spectral contrasts are low and retrievals using TES are less accurate, we use the VCM method, while over bare areas, where the VCM relies entirely on ancillary information, the TES method is preferred. The proposed methodology was applied to observations from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) onboard Meteosat Second Generation (MSG) satellites to derive emissivity channel and broad-band emissivities in the 3-14 μm range. Daily LSE maps are then derived using estimates of fraction of vegetation cover and snow cover. The product shows good agreement with in-situ data, with accuracies of 0.009 and 0.014 in the 8-14 μm and 3-8 μm regions, respectively. The methodology described in this article will be used to improve LST estimates and will be applied by the LSA-SAF for LST production from EUMETSAT’s Meteosat Second and Third Generation (MSG/MTG) and the Polar System-Second Generation (EPS-SG) missions.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
仔仔发布了新的文献求助10
1秒前
1秒前
彭于晏应助三星导弹船采纳,获得10
1秒前
斯文败类应助秋冥采纳,获得10
2秒前
完美的冷荷完成签到,获得积分10
2秒前
七星茶发布了新的文献求助30
3秒前
zm发布了新的文献求助10
3秒前
ding应助超级的夜白采纳,获得10
4秒前
郭立裕完成签到,获得积分20
4秒前
4秒前
5秒前
5秒前
5秒前
Zhaoyt完成签到,获得积分10
6秒前
Noah完成签到,获得积分10
6秒前
CodeCraft应助性静H情逸采纳,获得10
6秒前
NexusExplorer应助Vvvnnnaa1采纳,获得10
6秒前
外向的猫咪完成签到,获得积分10
7秒前
7秒前
Fa完成签到,获得积分10
7秒前
去码头整点薯条完成签到,获得积分10
7秒前
郭立裕发布了新的文献求助10
7秒前
robinhood完成签到,获得积分10
8秒前
依风发布了新的文献求助10
8秒前
闫111发布了新的文献求助10
9秒前
火腿完成签到,获得积分10
9秒前
晞嘻发布了新的文献求助10
9秒前
9秒前
我是老大应助梨子采纳,获得10
9秒前
10秒前
11秒前
小狗鼻噶发布了新的文献求助10
11秒前
张恺琦完成签到,获得积分10
11秒前
lgm发布了新的文献求助10
11秒前
11秒前
chen完成签到,获得积分10
11秒前
12秒前
12秒前
13秒前
脑洞疼应助dawn采纳,获得10
13秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Kinesiophobia : a new view of chronic pain behavior 3000
Les Mantodea de guyane 2500
Molecular Biology of Cancer: Mechanisms, Targets, and Therapeutics 2000
What is the Future of Psychotherapy in a Digital Age? 700
The Psychological Quest for Meaning 600
Zeolites: From Fundamentals to Emerging Applications 600
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 计算机科学 有机化学 物理 生物化学 纳米技术 复合材料 内科学 化学工程 人工智能 催化作用 遗传学 数学 基因 量子力学 物理化学
热门帖子
关注 科研通微信公众号,转发送积分 5955950
求助须知:如何正确求助?哪些是违规求助? 7170567
关于积分的说明 15940413
捐赠科研通 5090919
什么是DOI,文献DOI怎么找? 2736016
邀请新用户注册赠送积分活动 1696782
关于科研通互助平台的介绍 1617390