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Remote Sensing Applications-Society and Environment Q2

期刊收录：
ESCI
Scopus

基本信息

期刊ISSN：
2352-9385
期刊简拼：
REMOTE SENS APPL
年发文章数：
215
E-ISSN：
Gold OA文章占比
21.70%
研究文章占比：
96.28%
是否OA：
Yes
Jcr分区：
Q2
中科院分区：

出版信息

出版商：
ELSEVIER
涉及研究方向：
Multiple-
出版国家：
NETHERLANDS
出版语言：
English
出版周期：
Quarterly
出版年份：
2015

2023-2024最新影响因子:3.8
自引率:5.30%
五年影响因子:0
JCI期刊引文指标:0.82
h-index:暂无h-index数据
CiteScore:8.00

期刊关键词

2区 ESCI Scopus

期刊简介

Remote Sensing of Environment (RSE) serves the Earth observation community with the publication of results on the theory, science, applications, and technology of studies contributing to advance the science of remote sensing. Thoroughly interdisciplinary, RSE publishes on terrestrial, oceanic and atmospheric sensing. The emphasis of the journal is on biophysical and quantitative approaches to remote sensing at local to global scales and covers a wide range of applications and techniques:

Applications
• Land cover mapping, vegetation species identification and mapping

• Land surface energy and water balance

• Disturbance (fire, insect, harvest)

• Agriculture (crop mapping, yield prediction, phenology, soil properties, management practices)

• Forest and rangeland productivity and inventories

• Ecological applications & Ecosystem services (wetland, biodiversity, habitat, animal population, etc.)

• Urban applications (mapping, energy consumption, population, etc.)

• Terrestrial ecosystem productivity and carbon cycles

• Soil properties (moisture, organic matter, texture, structure, etc.)

• Geological Applications (minerals, landslide, subsidence, geomorphology, earth quake, etc.)

• Hydrology and water resources

• Inland and coastal waters

• Oceanography and marine science

• Cryosphere, mapping and modelling

• Atmospheric science and meteorology

• Snow, ice and glaciers

Techniques & Methods
• Feature extraction from RS images: segmentation and classification, surface structural, biochemical or physiological traits estimation from RS data

• Radiative transfer modelling

• Machine and deep learning for RS data analysis

• RS Data assimilation

• Satellite time series analysis & change detection

• Satellite data fusion (spectral, spatial and temporal)

• Satellite sensor systems characterization including radiometric and geometric calibration

• New remote sensing missions and systems