Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
8.3
4.2
Journals
Remote Sensing
Special Issues
In Situ Data in the Interplay of Remote Sensing
remotesensing-logo
Submit to Remote Sensing Review for Remote Sensing Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

In Situ Data in the Interplay of Remote Sensing

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

Deadline for manuscript submissions: closed (31 October 2022) | Viewed by 20616

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

Special Issue Information

Dear Colleagues,

The situation of remote sensing has changed fundamentally 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. An essential pre-requisite 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, moreover, 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 in context with the atmosphere, hydrosphere or biosphere.

For this Special Issue, we welcome the submission of manuscripts dealing with all aspects of the provision of in-situ data for the interpretation and evaluation of remote sensing data. The focus of interest 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 useful for calibrating, validating, 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
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.

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

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

Published Papers (7 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Editorial

Jump to: Research

4 pages, 194 KiB  
Editorial
Editorial for Special Issue: “In Situ Data in the Interplay of Remote Sensing”
by Mona Morsy, Erik Borg and Peter Dietrich
Remote Sens. 2023, 15(8), 2056; https://doi.org/10.3390/rs15082056 - 13 Apr 2023
Viewed by 1296
Abstract
The importance of remote sensing in solving challenges in rural and undeveloped areas where there is a lack of in situ data or financial resources is undeniable [...] Full article
(This article belongs to the Special Issue In Situ Data in the Interplay of Remote Sensing)

Research

Jump to: Editorial

18 pages, 5287 KiB  
Article
A Semi-Empirical Retrieval Method of Above-Ground Live Forest Fuel Loads by Combining SAR and Optical Data
by Yanxi Li and Binbin He
Remote Sens. 2023, 15(1), 5; https://doi.org/10.3390/rs15010005 - 20 Dec 2022
Cited by 7 | Viewed by 2966
Abstract
Forest fuel load is the key factor for fire risk assessment, firefighting, and carbon emissions estimation. Remote sensing technology has distinct advantages in fuel load estimation due to its sensitivity to biomass and adequate spatiotemporal observations for large scales. Many related works applied [...] Read more.
Forest fuel load is the key factor for fire risk assessment, firefighting, and carbon emissions estimation. Remote sensing technology has distinct advantages in fuel load estimation due to its sensitivity to biomass and adequate spatiotemporal observations for large scales. Many related works applied empirical methods with individual satellite observation data to estimate fuel load, which is highly conditioned on local data and limited by saturation problems. Here, we combined optical data (i.e., Landsat 7 ETM+) and spaceborne Synthetic Aperture Radar (SAR) data (i.e., ALOS PALSAR) in a proposed semi-empirical retrieval model to estimate above-ground live forest fuel loads (FLAGL). Specifically, optical data was introduced into water cloud model (WCM) to compensate for vegetation coverage information. For comparison, we also evaluated the performance of single spaceborne L-band SAR data (i.e., ALOS PALSAR) in fuel load estimation with common WCM. The above two comparison experiments were both validated by field measurements (i.e., BioSAR-2008) and leave-one-out cross-validation (LOOCV) method. WCM with single SAR data could achieve reasonable performance (R2 = 0.64 or higher and RMSEr = 35.3% or lower) but occurred an underestimation problem especially in dense forests. The proposed method performed better with R2 increased by 0.05–0.13 and RMSEr decreased by 5.8–12.9%. We also found that the underestimation problem (i.e., saturation problem) was alleviated even when vegetation coverage reached 65% or the total FLAGL reached about 183 Tons/ha. We demonstrated our FLAGL estimation method by validation in an open-access dataset in Sweden. Full article
(This article belongs to the Special Issue In Situ Data in the Interplay of Remote Sensing)
►▼ Show Figures

Figure 1

23 pages, 6114 KiB  
Article
Optimal Rain Gauge Network Design Aided by Multi-Source Satellite Precipitation Observation
by Helong Wang, Wenlong Chen, Zukang Hu, Yueping Xu and Dingtao Shen
Remote Sens. 2022, 14(23), 6142; https://doi.org/10.3390/rs14236142 - 3 Dec 2022
Cited by 2 | Viewed by 1659
Abstract
Optimized rain gauge networks minimize their input and maintenance costs. Satellite precipitation observations are particularly susceptible to the effects of terrain elevation, vegetation, and other topographical factors, resulting in large deviations between satellite and ground-based precipitation data. Satellite precipitation observations are more inaccurate [...] Read more.
Optimized rain gauge networks minimize their input and maintenance costs. Satellite precipitation observations are particularly susceptible to the effects of terrain elevation, vegetation, and other topographical factors, resulting in large deviations between satellite and ground-based precipitation data. Satellite precipitation observations are more inaccurate where the deviations change more drastically, indicating that rain gauge stations should be utilized at these locations. This study utilized satellite precipitation observation data to facilitate rain gauge network optimization. The deviations between ground-based precipitation data and three types of satellite precipitation observation data were used for entropy estimation. The rain gauge network in the Oujiang River Basin of China was optimally designed according to the principle of maximum joint entropy. Two optimization schemes of culling and supplementing 40 existing sites and 35 virtual sites were explored. First, the optimization and ranking of the rain gauge station network showed good stability and consistency. In addition, the joint entropy of deviation was larger than that of ground-based precipitation data alone, leading to a higher degree of discrimination between rain gauge stations and enabling the use of deviation data instead of ground-based precipitation data to assist network optimization, with more reasonable and interpretable results. Full article
(This article belongs to the Special Issue In Situ Data in the Interplay of Remote Sensing)
►▼ Show Figures

Graphical abstract

15 pages, 3701 KiB  
Article
Monitoring and Integrating the Changes in Vegetated Areas with the Rate of Groundwater Use in Arid Regions
by Mona Morsy, Silas Michaelides, Thomas Scholten and Peter Dietrich
Remote Sens. 2022, 14(22), 5767; https://doi.org/10.3390/rs14225767 - 15 Nov 2022
Cited by 3 | Viewed by 2342
Abstract
Frequent water table measurements are crucial for sustainable groundwater management in arid regions. Such monitoring is more important in areas that are already facing an acute problem with excessive groundwater withdrawal. In the majority of these locations, continuous readings of groundwater levels are [...] Read more.
Frequent water table measurements are crucial for sustainable groundwater management in arid regions. Such monitoring is more important in areas that are already facing an acute problem with excessive groundwater withdrawal. In the majority of these locations, continuous readings of groundwater levels are lacking. Therefore, an approximate estimate of the rate of increase or decrease in water consumption over time may serve as a proxy for the missing data. This could be achieved by tracking the changes in vegetated areas that generally correlate with changes in the rate of water use. The technique proposed in this paper is based on two remote sensing datasets: Landsat 7 and 8 from 2001 to 2021, and Sentinel 2A from 2015 to 2021, as well as five vegetation indices: Normalized Difference Vegetation Index (NDVI), Renormalized Difference Vegetation Index (RDVI), Soil Adjusted Vegetation Index (SAVI), Enhanced Vegetation Index (EVI), and Transformed Vegetation Index (TVI). The findings have shown that the datasets chosen performed best for small-scale land farms at the research location, which was chosen to be the El-Qaa plain, in the southwestern corner of the Sinai Peninsula in Egypt. Landsat 7 data with a resolution of 30 m revealed a substantial increase in land farms from 2.9 km2 in 2001 to 23.3 km2 in 2021. By using the five indices based on Sentinel 2A data, vegetated areas were categorized as heavy, moderate, or light. In addition, the expansion of each class area from 2015 to 2021 was tracked. Additionally, the NDVI index was modified to better reflect the arid environment (subsequently naming this new index as the Arid Vegetation Index: AVI). Rough scenarios of the increase in water consumption rate at the research site were generated by observing the increase in vegetated areas and collecting rough information from the farmers regarding the crop types. Full article
(This article belongs to the Special Issue In Situ Data in the Interplay of Remote Sensing)
►▼ Show Figures

Graphical abstract

18 pages, 7623 KiB  
Article
Alert-Driven Community-Based Forest Monitoring: A Case of the Peruvian Amazon
by Christina Cappello, Arun Kumar Pratihast, Alonso Pérez Ojeda del Arco, Johannes Reiche, Veronique De Sy, Martin Herold, Rolando Eduardo Vivanco Vicencio and Daniel Castillo Soto
Remote Sens. 2022, 14(17), 4284; https://doi.org/10.3390/rs14174284 - 30 Aug 2022
Cited by 4 | Viewed by 3237
Abstract
Community-based monitoring (CBM) is one of the- most sustainable ways of establishing a national forest monitoring system for successful Reduce Emissions from Deforestation and Forest Degradation (REDD+) implementation. In this research, we present the details of the National Forest Conservation Program (PNCB—Programa Nacional [...] Read more.
Community-based monitoring (CBM) is one of the- most sustainable ways of establishing a national forest monitoring system for successful Reduce Emissions from Deforestation and Forest Degradation (REDD+) implementation. In this research, we present the details of the National Forest Conservation Program (PNCB—Programa Nacional de Conservación de Bosques para la Mitigación del Cambio Climático), Peru, from a satellite-based alert perspective. We examined the community’s involvement in forest monitoring and investigated the usability of 1853 CBM data in conjunction with 445 satellite-based alerts. The results confirm that Peru’s PCNB contributed significantly to the REDD+ scheme, and that the CBM data provided rich information on the process and drivers of forest change. We also identified some of the challenges faced in the existing system, such as delays in satellite-based alert transfer to communities, sustaining community participation, data quality and integration, data flow, and standardization. Furthermore, we found that mobile devices responding to alerts provided better and faster data on land-use, and a better response rate, and facilitated a more targeted approach to monitoring. We recommend expanding training efforts and equipping more communities with mobile devices, to facilitate a more standardized approach to forest monitoring. The automation and unification of the alert data flow and incentivization of the participating communities could further improve forest monitoring and bridge the gap between near-real-time (NRT) satellite-based and CBM systems. Full article
(This article belongs to the Special Issue In Situ Data in the Interplay of Remote Sensing)
►▼ Show Figures

Figure 1

18 pages, 4399 KiB  
Article
Optimization of Rain Gauge Networks for Arid Regions Based on Remote Sensing Data
by Mona Morsy, Ruhollah Taghizadeh-Mehrjardi, Silas Michaelides, Thomas Scholten, Peter Dietrich and Karsten Schmidt
Remote Sens. 2021, 13(21), 4243; https://doi.org/10.3390/rs13214243 - 22 Oct 2021
Cited by 8 | Viewed by 2922
Abstract
Water depletion is a growing problem in the world’s arid and semi-arid areas, where groundwater is the primary source of fresh water. Accurate climatic data must be obtained to protect municipal water budgets. Unfortunately, the majority of these arid regions have a sparsely [...] Read more.
Water depletion is a growing problem in the world’s arid and semi-arid areas, where groundwater is the primary source of fresh water. Accurate climatic data must be obtained to protect municipal water budgets. Unfortunately, the majority of these arid regions have a sparsely distributed number of rain gauges, which reduces the reliability of the spatio-temporal fields generated. The current research proposes a series of measures to address the problem of data scarcity, in particular regarding in-situ measurements of precipitation. Once the issue of improving the network of ground precipitation measurements is settled, this may pave the way for much-needed hydrological research on topics such as the spatiotemporal distribution of precipitation, flash flood prevention, and soil erosion reduction. In this study, a k-means cluster analysis is used to determine new locations for the rain gauge network at the Eastern side of the Gulf of Suez in Sinai. The clustering procedure adopted is based on integrating a digital elevation model obtained from The Shuttle Radar Topography Mission (SRTM 90 × 90 m) and Integrated Multi-Satellite Retrievals for GPM (IMERG) for four rainy events. This procedure enabled the determination of the potential centroids for three different cluster sizes (3, 6, and 9). Subsequently, each number was tested using the Empirical Cumulative Distribution Function (ECDF) in an effort to determine the optimal one. However, all the tested centroids exhibited gaps in covering the whole range of elevations and precipitation of the test site. The nine centroids with the five existing rain gauges were used as a basis to calculate the error kriging. This procedure enabled decreasing the error by increasing the number of the proposed gauges. The resulting points were tested again by ECDF and this confirmed the optimum of thirty-one suggested additional gauges in covering the whole range of elevations and precipitation records at the study site. Full article
(This article belongs to the Special Issue In Situ Data in the Interplay of Remote Sensing)
►▼ Show Figures

Graphical abstract

19 pages, 2369 KiB  
Article
Comparative Analysis of TMPA and IMERG Precipitation Datasets in the Arid Environment of El-Qaa Plain, Sinai
by Mona Morsy, Thomas Scholten, Silas Michaelides, Erik Borg, Youssef Sherief and Peter Dietrich
Remote Sens. 2021, 13(4), 588; https://doi.org/10.3390/rs13040588 - 7 Feb 2021
Cited by 18 | Viewed by 3459
Abstract
The replenishment of aquifers depends mainly on precipitation rates, which is of vital importance for determining water budgets in arid and semi-arid regions. El-Qaa Plain in the Sinai Peninsula is a region that experiences constant population growth. This study compares the performance of [...] Read more.
The replenishment of aquifers depends mainly on precipitation rates, which is of vital importance for determining water budgets in arid and semi-arid regions. El-Qaa Plain in the Sinai Peninsula is a region that experiences constant population growth. This study compares the performance of two sets of satellite-based data of precipitation and in situ rainfall measurements. The dates selected refer to rainfall events between 2015 and 2018. For this purpose, 0.1° and 0.25° spatial resolution TMPA (Tropical Rainfall Measurement Mission Multi-satellite Precipitation Analysis) and IMERG (Integrated Multi-satellite Retrievals for Global Precipitation Measurement) data were retrieved and analyzed, employing appropriate statistical metrics. The best-performing data set was determined as the data source capable to most accurately bridge gaps in the limited rain gauge records, embracing both frequent light-intensity rain events and more rare heavy-intensity events. With light-intensity events, the corresponding satellite-based data sets differ the least and correlate more, while the greatest differences and weakest correlations are noted for the heavy-intensity events. The satellite-based records best match those of the rain gauges during light-intensity events, when compared to the heaviest ones. IMERG data exhibit a superior performance than TMPA in all rainfall intensities. Full article
(This article belongs to the Special Issue In Situ Data in the Interplay of Remote Sensing)
►▼ Show Figures

Graphical abstract

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-7
Back to TopTop