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Dr. Eric Vermote

NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA
Interests: radiative transfer; vicarious calibration; cloud/cloud shadow masking; atmospheric correction; vegetation monitoring (agriculture) from remote sensing

Dr. Jean-Claude Roger

E-Mail Website
Guest Editor

Department of Geographical Sciences, University of Maryland, College Park, MD 20740, USA
Interests: radiative transfer; vicarious calibration; cloud/cloud shadow masking; atmospheric correction; aerosol characterization (chemical, microphysical, physical, radiative) and aerosol radiative impact
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Special Issue Information

Dear Colleagues,

Recent advances in spaceborne remote sensing instruments with better spectral, spatial and radiometric capabilities have pushed product quality standards for better and traceable performances, and, therefore, the use of radiative transfer for their generations is a must.

Excellent review papers and books have been published on the topic of radiative transfer but mainly aimed at the experts in the field. With the advances and multiplications of satellite systems across the world and open and free data policies that are now a standard, the population of radiative transfer users has grown exponentially, also due to the exponential growth of computer capabilities.

A consistent review/discussion of the current state of the art in the use of radiative transfer modelling in remote sensing applications will be especially useful at this point to progress in the establishment of best practices, given the growing number of publications.

This Special Issue will collect original manuscripts on innovative research using state-of-the-art radiative transfer modelling in remote sensing applications. Articles on atmospheric correction, atmospheric parameters retrieval (e.g., aerosols), vegetation or water biophysical parameters retrievals are of particular interest but other relevant papers are welcome. The potential topics of this Special Issue include, but are not limited to the following:

Radiative transfer code validation in the context of specific remote sensing applications

Radiative transfer modelling and practical applications to remote sensing

Radiative transfer modelling in atmosphere application in remote sensing

Radiative transfer modelling for atmospheric correction in remote sensing

Radiative transfer modelling for remote sensing biophysical parameters retrieval (vegetation/water)

Dr. Eric Vermote
Dr. Jean-Claude Roger
Guest Editor

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

Radiative transfer
Remote sensing
Atmospheric correction
Aerosols
Biophysical variables
Atmosphere

Published Papers (2 papers)

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Research

14 pages, 3969 KiB

Open AccessArticle

Phenology Effects on Physically Based Estimation of Paddy Rice Canopy Traits from UAV Hyperspectral Imagery

by Li Wang, Shuisen Chen, Zhiping Peng, Jichuan Huang, Chongyang Wang, Hao Jiang, Qiong Zheng and Dan Li

Remote Sens. 2021, 13(9), 1792; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13091792 - 4 May 2021

Cited by 11 | Viewed by 3046

Abstract

Radiation transform models such as PROSAIL are widely used for crop canopy reflectance simulation and biophysical parameter inversion. The PROSAIL model basically assumes that the canopy is turbid homogenous media with a bare soil background. However, the canopy structure changes when crop growth stages develop, which is more or less a departure from this assumption. In addition, a paddy rice field is inundated most of the time with flooded soil background. In this study, field-scale paddy rice leaf area index (LAI), leaf cholorphyll content (LCC), and canopy chlorophyll content (CCC) were retrieved from unmanned-aerial-vehicle-based hyperspectral images by the PROSAIL radiation transform model using a lookup table (LUT) strategy, with a special focus on the effects of growth-stage development and soil-background signature selection. Results show that involving flooded soil reflectance as background reflectance for PROSAIL could improve estimation accuracy. When using a LUT with the flooded soil reflectance signature (

L U T_{f l o o d e d}

) the coefficients of determination (

R^{2}

) between observed and estimation variables are 0.70, 0.11, and 0.79 for LAI, LCC, and CCC, respectively, for the entire growing season (from tillering to heading growth stages), and the corresponding mean absolute errors (

M A E s

) are 21.87%, 16.27%, and 12.52%. For LAI and LCC, high model bias mainly occurred in tillering growth stages. There is an obvious overestimation of LAI and underestimation of LCC for in the tillering growth stage. The estimation accuracy of CCC is relatively consistent from tillering to heading growth stages. Full article

(This article belongs to the Special Issue Radiative Transfer Modelling in Remote Sensing Applications)

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28 pages, 3489 KiB

Open AccessArticle

Landsat-8 and Sentinel-2 Canopy Water Content Estimation in Croplands through Radiative Transfer Model Inversion

by Erik J. Boren and Luigi Boschetti

Remote Sens. 2020, 12(17), 2803; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12172803 - 29 Aug 2020

Cited by 12 | Viewed by 3540

Abstract

Despite the potential implications of a cropland canopy water content (CCWC) thematic product, no global remotely sensed CCWC product is currently generated. The successful launch of the Landsat-8 Operational Land Imager (OLI) in 2012, Sentinel-2A Multispectral Instrument (MSI) in 2015, followed by Sentinel-2B in 2017, make possible the opportunity for CCWC estimation at a spatial and temporal scale that can meet the demands of potential operational users. In this study, we designed and tested a novel radiative transfer model (RTM) inversion technique to combine multiple sources of a priori data in a look-up table (LUT) for inverting the NASA Harmonized Landsat Sentinel-2 (HLS) product for CCWC estimation. This study directly builds on previous research for testing the constraint of the leaf parameter (N_s) in PROSPECT, by applying those constraints in PRO4SAIL in an agricultural setting where the variability of canopy parameters are relatively minimal. In total, 225 independent leaf measurements were used to train the LUTs, and 102 field data points were collected over the 2015–2017 growing seasons for validating the inversions. The results confirm increasing a priori information and regularization yielded the best performance for CCWC estimation. Despite the relatively low variable canopy conditions, the inclusion of N_s constraints did not improve the LUT inversion. Finally, the inversion of Sentinel-2 data outperformed the inversion of Landsat-8 in the HLS product. The method demonstrated ability for HLS inversion for CCWC estimation, resulting in the first HLS-based CCWC product generated through RTM inversion. Full article

(This article belongs to the Special Issue Radiative Transfer Modelling in Remote Sensing Applications)

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