remotesensing-logo

Journal Browser

Journal Browser

New Advances in SmallSat and Landsat Algorithms and Applications for Earth Observation

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: 30 November 2025 | Viewed by 681

Special Issue Editors


E-Mail Website
Guest Editor
RESOLV, Inc., Hartford, SD 57033, USA
Interests: remote sensing; Landsat; smallsat data; atmospheric correction

Special Issue Information

Dear Colleagues,

Unfailingly, every practitioner of Earth observation celebrates the Landsat program’s contributions. No other single modern data resource has had such a widespread positive impact as the Landsat program. Today’s EO technology has been established through the Landsat effort, and its industry leadership is poised to continue with the launch of the greatly anticipated Landsat 10.

Smallsat constellations are now taking Landsat image processing technology into practical applications, making this a timely Special Issue. We encourage colleagues from around the globe to submit papers on the use of Landsat or smallsat imagery for Earth observation, including data calibration and validation. We look forward to original innovative papers addressing methods for image data processing and analysis, and the resulting practical applications for Earth observation across spatial and temporal scales alone, and in combination with other Earth observation data.

Dr. David P. Groeneveld
Dr. Hankui Zhang
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

  • Landsat
  • smallsats
  • small satellite constellations
  • analysis-ready data
  • time series analysis
  • scientific application
  • earth observation
  • calibration and validation

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.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

17 pages, 3563 KiB  
Article
A Phenology-Informed Framework for Detecting Deforestation in North Korea Using Fused Satellite Time-Series
by Yihua Jin, Jingrong Zhu, Zhenhao Yin, Weihong Zhu and Dongkun Lee
Remote Sens. 2025, 17(16), 2789; https://doi.org/10.3390/rs17162789 - 12 Aug 2025
Viewed by 158
Abstract
Accurate mapping of deforestation in regions characterized by complex, heterogeneous landscapes and frequent cloud cover remains a major challenge in remote sensing. This study presents a phenology-informed, spatiotemporal data fusion framework for robust deforestation mapping in North Korea, focusing particularly on hillside fields [...] Read more.
Accurate mapping of deforestation in regions characterized by complex, heterogeneous landscapes and frequent cloud cover remains a major challenge in remote sensing. This study presents a phenology-informed, spatiotemporal data fusion framework for robust deforestation mapping in North Korea, focusing particularly on hillside fields and unstocked forests—two dominant deforested land cover types in the region. By integrating multi-temporal satellite observations with variables derived from phenological dynamics, our approach effectively distinguishes spectrally similar classes that are otherwise challenging to separate. The Flexible Spatiotemporal Data Fusion Algorithm (FSDAF) was employed to generate high-frequency, Landsat-like time-series from MODIS data, thereby ensuring fine spatial detail alongside temporal consistency. Key classification features—including NDVI, NDSI, NDWI, and snowmelt timing—were identified and ranked using the Random Forest (RF) algorithm. The classification results were validated against reference Landsat imagery, achieving high correlation coefficients (R > 0.8) and structural similarity index values (SSIM > 0.85). The RF-based land cover classification reached an overall accuracy of 86.1% and a Kappa coefficient of 0.837, reflecting strong agreement with ground reference data. Comparative analyses demonstrated that this method outperformed global land cover products, such as MCD12Q1, in capturing the spatial variability and fragmented patterns of deforestation at the regional scale. This research underscores the value of combining spatiotemporal fusion with phenological indicators for accurate, high-resolution deforestation monitoring in data-limited environments, providing practical insights for sustainable forest management and ecological restoration planning. Full article
Show Figures

Graphical abstract

Back to TopTop