Challenges and Future Trends in Land Cover/Use Monitoring

A special issue of Land (ISSN 2073-445X). This special issue belongs to the section "Land – Observation and Monitoring".

Deadline for manuscript submissions: 31 October 2025 | Viewed by 825

Special Issue Editors


E-Mail Website
Guest Editor
Institute for Electromagnetic Sensing of the Environment (IREA), National Research Council (CNR) of Italy, 328 Diocleziano, 80124 Napoli, Italy
Interests: synthetic aperture radar (SAR) data processing; environmental remote sensing and applications; integration of SAR data from satellite and ground-based platforms; neural network-high performance computing (HPC)

E-Mail Website
Guest Editor
National Research Council (CNR), Institute of Methodologies for Environmental Analysis (IMAA), 85050 Tito Scalo, PZ, Italy
Interests: land-atmosphere processes; land change trade-offs for ecosystem services and biodiversity
Special Issues, Collections and Topics in MDPI journals

E-Mail
Guest Editor
Instituto Volcanológico de Canarias (INVOLCAN), Granadilla de Abona, Tenerife, Canary Islands, Spain
Interests: InSAR; dinsar; SBAS; comsol multiphysics; SAR interferometry; SARScape; GIS analysis

Special Issue Information

Dear Colleagues,

A new generation of remote sensing instruments mounted on-board drones, aerial and space vectors, which guarantee improved temporal sampling and enhanced spatial resolutions of collected data, has emerged in recent years. In this context, developing novel approaches for the effective processing of long-term sequences of remotely sensed data and the consolidation of traditional Earth observation methods is of great relevance. The exploitation of Earth observation methodologies is a common practice in the scientific community today. It allows researchers to conduct “in-depth” investigations regarding a vast amount of natural and anthropogenic phenomena leading to the deterioration of or changes in the Earth’s surface, soil erosion, vegetation disturbances and crop cover changes, with implications on agroforestry, agriculture, carbon farming, etc. Using novel high computing paradigms and developing new methods for integrating information derived from different sets of remotely sensed images acquired at complementary frequency bands also represent a new, challenging frontier in Earth observation techniques. Optical, microwave synthetic aperture radar and hyper-spectral data products are fostered. Several new RS satellite programs are being scheduled and deployed, and many new methods are being developed in concert. Constellations of satellite sensors working from microwave to optical wavelengths are systematically used to monitor land cover/use changes.

This Special Issue aims at collecting papers (original research articles and review papers) that provide insights into land monitoring.

This Special Issue welcomes manuscripts that link the following themes:
(1) High-resolution land mapping.
(2) Land cover hyperspectral imaging.
(3) Land data multi-source registration and fusion.
(4) Land use information/feature extraction.
(5) Land data pixel-level classification.
(6) Land change detection.
(7) High-performance computing.
(8) Synergic use of SAR and optical data for agricultural applications and/or for studying land use and land cover in imaged scenes.

We look forward to receiving your original research articles and reviews.

Dr. Antonio Pepe
Dr. Francesco Falabella
Dr. Rosa Coluzzi
Dr. Monika Przeor
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. Land is an international peer-reviewed open access monthly 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 2600 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 monitoring
  • land mapping
  • land cover
  • land use
  • land classification
  • land change detection
  • information fusion
  • multi-source registration
  • spectral imaging
  • machine learning
  • optical and microwave data

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, 9972 KiB  
Article
Improving Agricultural Efficiency of Dry Farmlands by Integrating Unmanned Aerial Vehicle Monitoring Data and Deep Learning
by Tung-Ching Su, Tsung-Chiang Wu and Hsin-Ju Chen
Land 2025, 14(6), 1179; https://doi.org/10.3390/land14061179 - 29 May 2025
Viewed by 335
Abstract
This study aimed to address the challenge of monitoring and managing soil moisture in dryland agriculture with supplemental irrigation under increasingly extreme climate conditions. Using unmanned aerial vehicles (UAVs) equipped with hyperspectral sensors, we collected imagery of wheat fields on Kinmen Island at [...] Read more.
This study aimed to address the challenge of monitoring and managing soil moisture in dryland agriculture with supplemental irrigation under increasingly extreme climate conditions. Using unmanned aerial vehicles (UAVs) equipped with hyperspectral sensors, we collected imagery of wheat fields on Kinmen Island at various growth stages. The Modified Perpendicular Drought Index (MPDI) was calculated to quantify soil drought conditions. Simultaneously, soil samples were collected to measure the actual soil moisture content. These datasets were used to develop a Gradient Boosting Regression (GBR) model to estimate soil moisture across the entire field. The resulting AI-based model can guide decisions on the timing and scale of supplemental irrigation, ensuring water is applied only when needed during crop growth. Furthermore, MPDI values and wheat spike samples were used to construct another GBR model for yield prediction. When applying MPDI values from multispectral imagery collected at a similar stage in the following year, the model achieved a prediction accuracy of over 90%. The proposed approach offers a reliable solution for enhancing the resilience and productivity of dryland crops under climate stress and demonstrates the potential of integrating remote sensing and machine learning in precision water management. Full article
(This article belongs to the Special Issue Challenges and Future Trends in Land Cover/Use Monitoring)
Show Figures

Figure 1

Back to TopTop