地球科学 | SCI期刊专刊信息1条

全文截稿: 2021-10-30

影响因子: 6.942

中科院JCR分区:

• 大类 : 工程技术 - 1区

• 小类 : 自然地理 - 1区

• 小类 : 地球科学综合 - 1区

• 小类 : 成像科学与照相技术 - 1区

• 小类 : 遥感 - 1区

网址: https://www.journals.elsevier.com/isprs-journal-of-photogrammetry-and-remote-sensing

In this regard several new generations of Earth Observing (EO) advanced spaceborne hyperspectral sensors were launched and others are in preparation. Recent launches include the German Aerospace Center’s (Deutsches Zentrum fur Luft- und Raumfahrt; DLR’s) Earth Sensing Imaging Spectrometer (DESIS) integrated into and onboard the International Space Station’s (ISS) Multi-integrated into User-System for Earth Sensing (MUSES) platform, the Italian Space Agency (Agenzia Spaziale Italiana, ASI’s) PRISMA (PRecursore IperSpettrale della Missione Applicativa), Japanese HISUI (Hyperspectral Imager Suite) onboard ISS, India’s HysIS (Hyperspectral Imaging Satellite), China’s Advanced Hyperspectral Imager (AHSI) aboard China's GaoFen-5 (GF-5) satellite, and China’s Jilin Hyperspectral Satellite constellation. These will be followed by several hyperspectral missions coming up for launch such as the German DLR’s the Environmental Mapping and Analysis Program (EnMAP) to be launched soon, NASA’s Surface Biology and Geology (SBG) mission (formerly HyspIRI mission), EMIT from NASA, CHIME from ESA FLEX of ESA, SHALOM from ASI-ISA and more others from private players. These sensors will collect data in hundreds of wavebands and, typically, across the entire visible near and short infrared spectral range (400 to 2500 nanometers ) or part of it, and in 30 m or better GDS. Such data captured as “spectral signatures” leading to spectral libraries will be (and is) a quantum leap in data of the Planet Earth relative to older generation multispectral sensors such as the Landsats and Sentinels which have provided great service in the study of the Planet Earth over last few decades. Nevertheless, such data also provides great challenges in terms of data analysis, algorithm development, and application development.

Given the above context, this special issue proposes a comprehensive study of agricultural crops and vegetation based on the data gathered from new generation of hyperspectral data gathered from multiple sensors andor comparison with hyperspectral data from myriad sensors and all spectral and altitude domains. Specific topics of interest include, but not limited to:

Quantitative analysis of agricultural crop biophysical, biochemical, and plant health parameters through hyperspectral narrow-bands (HNBs), hyperspectral vegetation indices (HVIs) and comparison with multispectral broadband derived vegetation indices;

Crop type classifications using HNB data and comparison with multi-spectral broadband (MBB) data showing improvements in accuracies and reduction in uncertainties;