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Remote Sensing
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In Situ Data in the Interplay of Remote Sensing II
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In Situ Data in the Interplay of Remote Sensing II

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

Deadline for manuscript submissions: closed (29 February 2024) | Viewed by 5433

Special Issue Editors

Prof. Dr. Peter Dietrich

E-Mail Website
Guest Editor
Department Monitoring and Exploration Technologies, Helmholtz Centre for Environmental Research GmbH - UFZ, 04318 Leipzig, Germany
Interests: development and evaluation of method and method combination for the monitoring of near surface processes; geophysics; imaging; data reliability; earth system science
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Erik Borg

E-Mail Website
Guest Editor
German Aerospace Center (DLR), German Remote Sensing Data Center (DFD), National Ground Segment, 17235 Neustrelitz, Germany
Interests: remote sensing; environment; spatial analysis; environmental impact assessment; climate change; satellite image analysis; satellite image processing; geospatial science; geoinformation, in-situ measurement strategies; mapping
Special Issues, Collections and Topics in MDPI journals
Dr. Mona Ahmad Mahmoud Morsy

E-Mail Website
Guest Editor
Geology Department, Faculty of Science, Suez Canal University, Ismailia 41522, Egypt
Interests: development and evaluation of arid areas by targeting climatic conditions that affect the main water resources there
Special Issues, Collections and Topics in MDPI journals
Dr. Mahmud Haghshenas Haghighi

E-Mail Website
Guest Editor
Institute of Photogrammetry and Geoinformation, Leibniz University Hannover, 30167 Hannover, Germany
Interests: interferometric SAR (InSAR); ground deformation; natural and anthropogenic hazards

Special Issue Information

Dear Colleagues,

The situation of remote sensing has fundamentally changed since 2000. An essential feature of this change is the transition from exemplary feasibility study to the continuous and operational availability and processing of remote sensing products. Essential pre-requisites for the secured valorisation of remote sensing data and derived value-added information products are in situ data on the structural and substantial environmental situation, which can be related to area-wide remote sensing data. Although in situ data acquisition is usually labor- and cost-intensive, and sometimes has to be carried out in a very limited time span in parallel with operational remote sensing, these data are necessary, for example, in order to understand and correctly interpret the interactions of interesting environmental phenomena with the atmosphere, hydrosphere or biosphere.

The previous Special Issue 'In Situ Data in the Interplay of Remote Sensing' was a great success. For this second volume, we welcome the submission of manuscripts that deal with all aspects of the provision of in situ data for the interpretation and evaluation of remote sensing data. The focus of this Special Isue is on the efficiency and operability of in situ data provision, as well as on the reliability, accuracy, objectivity or accessibility, timeliness, completeness, appropriate scope, and relevance of in situ data. In addition, of interest are measurement methods and measurement strategies, test sites, and national and international networks dedicated to data provision, data combinations, and the creation of historical time series that are useful for calibrating, validating and verifying remote sensing sensors, missions, processors, data, and value-added information products. Therefore, this call is also open for all related topics concerning Cal/Val activities.

Prof. Dr. Peter Dietrich
Prof. Dr. Erik Borg
Dr. Mona Ahmad Mahmoud Morsy
Dr. Mahmud Haghshenas Haghighi
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.

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (3 papers)

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Research

20 pages, 7155 KiB  
Article
Transferability of Machine Learning Models for Crop Classification in Remote Sensing Imagery Using a New Test Methodology: A Study on Phenological, Temporal, and Spatial Influences
by Hauke Hoppe, Peter Dietrich, Philip Marzahn, Thomas Weiß, Christian Nitzsche, Uwe Freiherr von Lukas, Thomas Wengerek and Erik Borg
Remote Sens. 2024, 16(9), 1493; https://doi.org/10.3390/rs16091493 - 23 Apr 2024
Cited by 2 | Viewed by 1366
Abstract
Machine learning models are used to identify crops in satellite data, which achieve high classification accuracy but do not necessarily have a high degree of transferability to new regions. This paper investigates the use of machine learning models for crop classification using Sentinel-2 [...] Read more.
Machine learning models are used to identify crops in satellite data, which achieve high classification accuracy but do not necessarily have a high degree of transferability to new regions. This paper investigates the use of machine learning models for crop classification using Sentinel-2 imagery. It proposes a new testing methodology that systematically analyzes the quality of the spatial transfer of trained models. In this study, the classification results of Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Stochastic Gradient Descent (SGD), Multilayer Perceptron (MLP), Support Vector Machines (SVM), and a Majority Voting of all models and their spatial transferability are assessed. The proposed testing methodology comprises 18 test scenarios to investigate phenological, temporal, spatial, and quantitative (quantitative regarding available training data) influences. Results show that the model accuracies tend to decrease with increasing time due to the differences in phenological phases in different regions, with a combined F1-score of 82% (XGBoost) when trained on a single day, 72% (XGBoost) when trained on the half-season, and 61% when trained over the entire growing season (Majority Voting). Full article
(This article belongs to the Special Issue In Situ Data in the Interplay of Remote Sensing II)
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24 pages, 13510 KiB  
Article
The Impact and Correction of Sensitive Environmental Factors on Spectral Reflectance Measured In Situ
by Huijie Zhao, Ziwei Wang, Guorui Jia, Jia Tian, Shuliang Jin, Shuneng Liang and Yumeng Liu
Remote Sens. 2023, 15(22), 5332; https://doi.org/10.3390/rs15225332 - 12 Nov 2023
Viewed by 1312
Abstract
The spectral reflectance measured in situ is often regarded as the “truth” of objects, which plays an important role in Earth observation applications. However, in situ measurements are influenced by several factors such as atmospheric conditions, illumination and view geometry (I&VG), cloud coverage, [...] Read more.
The spectral reflectance measured in situ is often regarded as the “truth” of objects, which plays an important role in Earth observation applications. However, in situ measurements are influenced by several factors such as atmospheric conditions, illumination and view geometry (I&VG), cloud coverage, and adjacency effects. In order to avoid the influence of these factors, in situ measurements are usually carried out under sunny days and close to noon. However, the impact of I&VG is still present in most cases. At present, people still know little about the influence mechanism of I&VG. Moreover, correcting the impact of I&VG is also a problem that needs to be urgently solved in reflectance spectroscopy. In this work, experiments are carried out using the multi-directional hyperspectral remote sensing simulation facility (MHSRS2F), which allows adjustment and control of the I&VG parameters. This paper proposes an uncertainty evaluation model for I&VG and quantifies the uncertainty caused by different I&VG parameters. Then, the sensitivity of reflectance to I&VG at different wavelengths is explored based on uncertainty models. Finally, a correction model for reflectance under different I&VG conditions is proposed. The results reveal that the uncertainty and sensitivity caused by observation height are relatively high, regardless of the surface heterogeneity. It directly affects the size of the field of view and the physicochemical characteristics of the object. For objects that approximate the Lambertian surface, more attention should be paid to the selection and variation of solar and view zenith angles and view azimuth angles. For objects with surface heterogeneity, the selection and variation of solar azimuth angle, view azimuth angle, and solar zenith angle are more crucial. The correction model proposed in this paper has a 41.25% correction effect on different view zenith angles, but the correction effect on other environmental factors is not significant. Full article
(This article belongs to the Special Issue In Situ Data in the Interplay of Remote Sensing II)
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24 pages, 1757 KiB  
Article
Choice of Solar Spectral Irradiance Model for Current and Future Remote Sensing Satellite Missions
by Fuqin Li, David L. B. Jupp, Brian L. Markham, Ian C. Lau, Cindy Ong, Guy Byrne, Medhavy Thankappan, Simon Oliver, Tim Malthus and Peter Fearns
Remote Sens. 2023, 15(13), 3391; https://doi.org/10.3390/rs15133391 - 3 Jul 2023
Cited by 1 | Viewed by 2141
Abstract
The accuracy of surface reflectance estimation for satellite sensors using radiance-based calibrations can depend significantly on the choice of solar spectral irradiance (or solar spectrum) model used for atmospheric correction. Selecting an accurate solar spectrum model is also important for radiance-based sensor calibration [...] Read more.
The accuracy of surface reflectance estimation for satellite sensors using radiance-based calibrations can depend significantly on the choice of solar spectral irradiance (or solar spectrum) model used for atmospheric correction. Selecting an accurate solar spectrum model is also important for radiance-based sensor calibration and estimation of atmospheric parameters from irradiance observations. Previous research showed that Landsat 8 could be used to evaluate the quality of solar spectrum models. This paper applies the analysis using five previously evaluated and three more recent solar spectrum models using both Landsat 8 (OLI) and Landsat 9 (OLI2). The study was further extended down to 10 nm resolution and a wavelength range from Ultraviolet A (UVA) to shortwave infrared (SWIR) (370–2480 nm) using inversion of field irradiance measurements. The results using OLI and OLI2 as well as the inversion of irradiance measurements were that the more recent Chance and Kurucz (SA2010), Meftah (SOLAR-ISS) and Coddington (TSIS-1) models performed better than all of the previous models. The results were illustrated by simulating dark and bright surface reflectance signatures obtained by atmospheric correction with the different solar spectrum models. The results showed that if the SA2010 model is assumed to be the “true” solar irradiance, using the TSIS-1 or the SOLAR-ISS model will not significantly change the estimated ground reflectance. The other models differ (some to a large extent) in varying wavelength areas. Full article
(This article belongs to the Special Issue In Situ Data in the Interplay of Remote Sensing II)
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