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Remote Sensing for Soil and Water Conservation and Sustainable Development in the Context of Climate Change

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

Deadline for manuscript submissions: 28 February 2026 | Viewed by 2159

Special Issue Editors


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Guest Editor
National Key Laboratory of Water Disaster Prevention, Nanjing Hydraulic Research Institute, Nanjing 210029, China
Interests: remote sensing of soil and water conservation; remote sensing of water environment; water resources management in response to climate change
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Research Center for Climate Change, Ministry of Water Resources, Nanjing 210029, China
Interests: hydrological modeling; water resources management; drought and flood management; remote sensing data analysis; climate change
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
South China Institute of Environmental Science, Ministry of Ecology and Environment, Guangzhou 510535, China
Interests: water color; water clarity; chlorophyll-a; suspended particulate matter; machine learning; non-optically active parameters

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Guest Editor
School of Geography and Remote Sensing, Guangzhou University, Guangzhou 510006, China
Interests: soil and water environmental remote sensing; machine/deep learning; vegetation phenology; lake ice phenology
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
School of Soil and Water Conservation, Nanchang Institute of Technology, Nanchang 330044, China
Interests: remote sensing for agricultural and forestry; prevention and control of soil erosion; water and soil conservation

Special Issue Information

Dear Colleagues,

The interplay between soil, water, and environmental resources is crucial for sustaining ecosystems and human livelihoods, especially against the backdrop of climate change. Remote sensing technologies have emerged as powerful tools to monitor and analyze these interactions at various scales, providing critical insights into the effectiveness of conservation practices and resource management strategies. This Special Issue seeks to delve into the role of remote sensing in understanding and addressing the challenges posed by climate change on soil and water conservation, natural resource management, and overall environmental health. We aim to gather innovative research that demonstrates the capabilities of remote sensing in assessing, planning, and implementing effective responses to climate-related impacts.

We invite original research articles, reviews, and case studies that focus on the integration of remote sensing techniques in studying soil and water conservation, resources, and environmental dynamics in the context of climate change. Papers that highlight interdisciplinary approaches and novel methodologies are especially welcome.

Potential topics include, but are not limited to:

  1. Remote sensing methodologies for monitoring soil and water conservation practices;
  2. Impacts of climate change and human activities on soil health and water resources;
  3. The role of remote sensing in policy formulation and implementation for soil and water resource management;
  4. Studies showcasing successful applications of remote sensing in addressing climate change challenges;
  5. Terrestrial ecosystem/net primary productivity of vegetation;
  6. Impacts of human land use management on desert environment/land degradation;
  7. Land degradation and rehabilitation based on intelligent geo-computing;
  8. Lake/river ice phenology dynamics monitoring/impacts of climate change on lake/river ice phenology;
  9. Non-optically active water parameters (e.g., phosphorus and nitrogen) algorithm development and dynamic monitoring;
  10. Machine/deep learning algorithm for remote sensing applications;
  11. Large or foundation models for a developed understanding of remote sensing imagery.

Dr. Shaohua Lei
Prof. Dr. Xiaojun Wang
Prof. Dr. Guoqing Wang
Dr. Shuai Zeng
Dr. Yongjian Ruan
Prof. Dr. Xianghui Lu
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

  • soil and water conservation
  • management techniques for soil and water loss
  • net primary productivity
  • aquatic ecosystems health
  • water quality parameters retrieval
  • non-optically active parameters
  • machine/deep learning
  • vegetation phenology
  • lake/river ice phenology
  • climate change and human activities impacts

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Published Papers (2 papers)

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Research

23 pages, 11853 KiB  
Article
GDPGO-SAM: An Unsupervised Fine Segmentation of Desert Vegetation Driven by Grounding DINO Prompt Generation and Optimization Segment Anything Model
by Shuzhen Hua, Biao Yang, Xinchang Zhang, Ji Qi, Fengxi Su, Jing Sun and Yongjian Ruan
Remote Sens. 2025, 17(4), 691; https://doi.org/10.3390/rs17040691 - 18 Feb 2025
Viewed by 622
Abstract
Desert encroachment significantly threatens the living and activity space of humanity, and undertaking human-directed vegetation restoration is one of the effective ways to prevent desert expansion. In the process of desert vegetation restoration, counting the number of tree saplings for rapidly assessing the [...] Read more.
Desert encroachment significantly threatens the living and activity space of humanity, and undertaking human-directed vegetation restoration is one of the effective ways to prevent desert expansion. In the process of desert vegetation restoration, counting the number of tree saplings for rapidly assessing the survival rate of vegetation (such as Haloxylon ammodendron) is a critical task within the restoration process. However, traditional ground-based statistical methods are resource-intensive and time-consuming. This paper proposed a novel unsupervised fine segmentation framework driven by Grounding DINO prompt generation and optimization segment anything model, termed GDPGO-SAM, designed for the segmentation of desert vegetation from UAV-derived remote sensing imagery, thereby facilitating the rapid inventory of tree saplings counts. The framework combines the Grounding DINO object detector and the pre-trained visual model SAM, employing a task-prior-based prompt optimization mechanism to effectively capture the innate features of desert vegetation. This method achieves zero-sample instance segmentation of desert vegetation with an overall accuracy (OA) of 96.56%, a mean Intersection over Union (mIoU) of 81.50%, and a kappa coefficient (kappa) of 0.782, successfully overcoming the limitations of traditional supervised models that rely on passive memorization rather than true recognition. This research significantly enhances the precision of vegetation extraction and canopy depiction, providing strong support for the management of desert vegetation restoration and combating desert expansion. Full article
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21 pages, 12064 KiB  
Article
Long Time Series Spatiotemporal Variations in NPP Based on the CASA Model in the Eco-Urban Agglomeration Around Poyang Lake, China
by Tianmeng Du, Fei Yang, Jun Li, Chengye Zhang, Kuankuan Cui and Junxi Zheng
Remote Sens. 2025, 17(1), 80; https://doi.org/10.3390/rs17010080 - 28 Dec 2024
Viewed by 913
Abstract
The ecological urban agglomeration around Poyang Lake represents a critical development area in the Yangtze River basin. The spatiotemporal characteristics of the net primary productivity (NPP) of vegetation are explored from the perspective of the city’s functional position, providing important insights for the [...] Read more.
The ecological urban agglomeration around Poyang Lake represents a critical development area in the Yangtze River basin. The spatiotemporal characteristics of the net primary productivity (NPP) of vegetation are explored from the perspective of the city’s functional position, providing important insights for the city to achieve the dual-carbon target and green development. The study evaluates the spatiotemporal variations in NPP from 2003 to 2022 in the eco-urban agglomeration around Poyang Lake, using the CASA model. Its variation characteristics were explored in detail from a completely new perspective and scope using indicators such as cycle amplitudes, CV coefficients, Hurst indices, and others. Results indicate seasonal fluctuations and significant variations between urban areas and vegetation, with implications for sustainable development. The annual NPP ranged from 200 to 800 gC/(m2·a), with a change rate of 0.58 gC/(m2·a) and evident seasonal fluctuations in the study area. Notably, urban core cities like Jiujiang and Nanchang exhibit lower NPP and decreasing trends. Scenic areas showed high forest cover and vigorous NPP changes, highlighting the need for targeted urban ecological management to enhance green development. Additionally, the seasonal fluctuations in NPP were notably influenced by specific land use types and local economic conditions. In areas with high vegetation cover, the seasonal characteristics of NPP are pronounced, while they are less evident in regions with strong urban economic conditions. Full article
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