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Advances in Quantitative Remote Sensing

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Engineering Remote Sensing".

Deadline for manuscript submissions: closed (30 September 2022) | Viewed by 2433

Special Issue Editors

Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing, China
Interests: thermal infrared remote sensing; scaling and validation of remote sensed products; retrieval of hydrothermal parameters from remote sensing data
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The large number of remote sensing observations enable us to quantitatively describe the dynamics of surface variables with high temporal and spatial resolution, which is very important for surface monitoring and modelling from regional to global scale. With the advances in our understanding of the physical radiative transfer processes, the traditional quantitative remote sensing methods are gradually changing from empirical statistical methods to theoretical methods. With the development of computer science, the retrieval methods may be developed from model-driven to data-driven or both. In recent years, artificial intelligence technology has also attracted extensive attention. In addition, more and more quantitative remote sensing products, containing various biophysical and biochemical parameters, are widely used in environmental and ecological research. In general, great progress has been made in the field of quantitative remote sensing in recent decades.

This Special Issue aims to study the state-of-the-art of methodology of land surface parameter retrieval and validation, as well as the further quantitative analysis or applications of multi-platform remote sensing observations, including but not limited to:

  • Radiative transfer theory and model development.
  • Theory and methodology for parameter retrieval.
  • Progress in scale effect and scaling method.
  • Comprehensive validation and evaluation of remote sensing products.
  • Novel multi-source data fusion methodology.
  • Spatial and temporal variation patterns of a specific parameter and its driving force.

Prof. Dr. Zhaoliang Li
Dr. Hua Wu
Prof. Dr. José A. Sobrino
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • land surface parameter retrieval
  • radiative transfer model
  • scale effect and scaling
  • multi-source data fusion
  • model evaluation
  • product validation
  • spatiotemporal analysis

Published Papers (1 paper)

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Research

21 pages, 8429 KiB  
Article
Evaluation of Performance of Three Satellite-Derived Precipitation Products in Capturing Extreme Precipitation Events over Beijing, China
by Yu Li, Bo Pang, Meifang Ren, Shulan Shi, Dingzhi Peng, Zhongfan Zhu and Depeng Zuo
Remote Sens. 2022, 14(11), 2698; https://doi.org/10.3390/rs14112698 - 04 Jun 2022
Cited by 8 | Viewed by 1888
Abstract
Extreme precipitation events have a more serious impact on densely populated cities and therefore reliable estimation of extreme precipitation is very important. Satellite-derived precipitation products provide precipitation datasets with high spatiotemporal resolution. For improved applicability to estimating urban extreme precipitation, the performance of [...] Read more.
Extreme precipitation events have a more serious impact on densely populated cities and therefore reliable estimation of extreme precipitation is very important. Satellite-derived precipitation products provide precipitation datasets with high spatiotemporal resolution. For improved applicability to estimating urban extreme precipitation, the performance of such products must be evaluated regionally. This study evaluated three satellite-derived precipitation products, the Integrated Multi-satellite Retrievals for GPM (IMERG_V06), Multi-Source Weighted-Ensemble Precipitation (MSWEP V2), and China Meteorological Forcing Dataset (CMFD), in capturing extreme precipitation using observations acquired at 36 rainfall stations during 2001–2016 in Beijing, China. Results showed that MSWEP had the highest accuracy regarding daily precipitation data, with the highest correlation coefficient and the lowest absolute deviation between MSWEP and the rainfall station observations. CMFD demonstrated the best ability for correct detection of daily precipitation events, while MSWEP maintained the lowest rate of detecting non-rainy days as rainy days. MSWEP performed better in estimating precipitation amount and the number of precipitation days when daily precipitation was <50 mm; CMFD performed better when daily precipitation was >50 mm. All three products underestimated extreme precipitation. The Structural Similarity Index, which is a map comparison technique, was used to compare the similarities between the three products and rainfall station observations of two extreme rainstorms: “7.21” in 2012 and “7.20” in 2016. MSWEP and CMFD showed higher levels of similarity in terms of spatial–temporal structure. Overall, despite systematic underestimation, MSWEP performed better than IMERG and CMFD in estimating extreme precipitation in Beijing. Full article
(This article belongs to the Special Issue Advances in Quantitative Remote Sensing)
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